Speaker 1: Welcome to the Deep Dive. We're the show that gets
Speaker 1: you quickly and thoroughly well informed on the topics that
Speaker 1: really matter. And today we are plunging headfirst into something that, honestly,
Speaker 1: it still feels a bit like science fiction, but it's
Speaker 1: very much real now, the world of artificial intelligence, specifically
Speaker 1: large language models or LMS. I mean, think about it.
Speaker 1: I remember when chat GPT just exploded onto the scene exactly.
Speaker 1: It wasn't just some tech news item. It went global,
Speaker 1: pulling what over one hundred million users in just two months.
Speaker 1: It really felt like these things could just conjure up text, answers,
Speaker 1: anything right out of thin air.
Speaker 2: It did feel a bit magical, didnety totally.
Speaker 1: So our mission with this Deep Dive is really to
Speaker 1: give you a shortcut into learning liang chain, building AI
Speaker 1: and LM applications. We want to go beyond the buzzwords,
Speaker 1: you know, get into the core concepts, the actual practical
Speaker 1: strategies for building powerful AI apps with this thing called
Speaker 1: lang chain. Our goal isn't just to tell you what's
Speaker 1: happening in AI, but really show you why it matters,
Speaker 1: point out the aha moments, and crucially equip you the
Speaker 1: listener with the knowledge to maybe even apply it yourself.
Speaker 2: And to guide us through this pretty intricate, in let's
Speaker 2: be honest, rapidly evolving landscape. We're drawing directly from a
Speaker 2: really fantastic source, the book Learning lang Chain by myo
Speaker 2: Ocean and Nunocampos. And these aren't just you know, academics
Speaker 2: writing about the fields from Afar. Mayo was actually an
Speaker 2: early developer, an advocate for the lang Chain open source
Speaker 2: library itself, a real pioneer in that whole chat with
Speaker 2: data movement, and Nun is a founding software engineer at
Speaker 2: lang Chain. So this book, it isn't just theory, it's
Speaker 2: packed with super clear explanations, actionable techniques. Industry experts are
Speaker 2: calling it the go to resource for building production ready
Speaker 2: generative AI and agents.
Speaker 1: That's fantastic, a really solid foundation. Then, So the big
Speaker 1: question we're tackling today is this, how can developers, maybe
Speaker 1: even those who don't have a deep machine learning background,
Speaker 1: how can they harness this incredible power of lllms to
Speaker 1: build genuinely production ready generative AI applications and these intelligent agents. Right,
Speaker 1: we're going to unpack the essential tools, the patterns, the
Speaker 1: thinking that transforms these powerful models from cool tech demos
Speaker 1: into practical, usable solutions. So, okay, let's start right at
Speaker 1: the beginning. These lms, they seem almost magical. How exactly
Speaker 1: do they know the answers they give and what precisely
Speaker 1: is a token in their world?
Speaker 3: Yeah, good place to start.
Speaker 2: So at their heart, large language models are generative models
Speaker 2: specifically built for text. They're trained on just vast amounts
Speaker 2: of text, think everything publicly available, books, articles, forums, code,
Speaker 2: even cleaned up video transcripts, an immense data set. Their
Speaker 2: core function isn't really magic, though it looks like it.
Speaker 2: It's incredibly sophisticated prediction. They basically predict the most probable
Speaker 2: next word or token in a sequence based on all
Speaker 2: the patterns they've learned. So if you feed it the
Speaker 2: capital of England is, it's learned from countless examples that
Speaker 2: the highest probability next word is.
Speaker 1: London, ok N, matching on a massive scale. But what's
Speaker 1: a token? Is it just a word?
Speaker 3: Not always?
Speaker 2: Yeah, a token is the fundamental unit the LM processes.
Speaker 2: Often it's a word, but sometimes longer or less common
Speaker 2: words get broken down, like dearest might become two tokens.
Speaker 2: Done and arrest on average. You know, for common English text,
Speaker 2: one token is roughly four characters. And the driving engine
Speaker 2: behind all this predictive power is something called the transformer
Speaker 2: neural network architecture.
Speaker 1: Right, the transformer architecture.
Speaker 2: Heard a lot about that, Yeah, it's key. Think of
Speaker 2: it as being really good at understanding context. It relates
Speaker 2: every word in a sentence to every other word, building
Speaker 2: this rich understanding of meaning and relationships. That's how they
Speaker 2: handle complex grammar and nuance, not just simple word prediction.
Speaker 1: And it was that understanding, or perhaps a limitation of
Speaker 1: that understanding, that led to lang chain, Right, I read
Speaker 1: Harrison Chase started the open source library back in October
Speaker 1: twenty twenty two. What was the key realization.
Speaker 2: Exactly, the real breakthrough, The thing that sparked lang chain
Speaker 2: was this insight in LM, as brilliant as it is
Speaker 2: with language, could totally fumble basic arithmetic, Like ask it
Speaker 2: to calculate one two hundred and thirty four module one
Speaker 2: twenty three on its own. It might just guess or
Speaker 2: get it wrong.
Speaker 1: Huh weird. Right, It can write poetry but not do
Speaker 1: simple math.
Speaker 3: It is kind of paradoxical.
Speaker 2: Yeah, And that raised this crucial question, how do you
Speaker 2: give this powerful language model capabilities it just doesn't have intrinsically.
Speaker 2: Harrison Chase's pivotal realization was, and this is key, the
Speaker 2: most interesting LLM applications needed to use lms together with
Speaker 2: other sources of computation or knowledge. Laying Chain was essentially
Speaker 2: built to provide the building blocks, the interfaces, and the
Speaker 2: tooling to reliably combine llms with other things, like giving
Speaker 2: it the ability to call out to a calculator when
Speaker 2: it sees a math problem.
Speaker 1: That makes so much sense giving it tools. So, if
Speaker 1: the LLM isn't a calculator or a database itself, how
Speaker 1: do we even talk to it effectively? How do we
Speaker 1: guide it to do what we need?
Speaker 2: Yeah, that's all about prompting. The prompt is basically the
Speaker 2: instructions and input you provide to the model, and crucially,
Speaker 2: how you phrase that prompt significantly influences the model's output.
Speaker 2: There's also this fascinating control called temperature. You can think
Speaker 2: of it like a creativity dial. Lower temperature makes the
Speaker 2: output more focused, more deterministic, predictable. Higher temperature it lets
Speaker 2: the model take more risks, get more creative, maybe even
Speaker 2: a bit random, useful for different tasks.
Speaker 1: Okay, so prompting is key, temperature, controls, creativity. What are
Speaker 1: the main ways we prompt these things?
Speaker 2: There are several core techniques, each kind of addressing a
Speaker 2: different need. The absolute simplest is zero shot prompting. Just
Speaker 2: give it a direct instruction like that example earlier, how
Speaker 2: Old was the thirtiest president of the United States when
Speaker 2: his wife's mother died. It's straightforward for basic questions, but
Speaker 2: you know it can often lead to inaccuracies or just
Speaker 2: making things up at the info is and baked into
Speaker 2: his training data.
Speaker 1: Hallucinations, right, the dreaded hallucinations joys lely.
Speaker 2: Then you've got chain of thought or co T. This
Speaker 2: is where you literally tell the model think step by step.
Speaker 2: It's intriguing because this often dramatically improves performance on reasoning tasks.
Speaker 2: It forces the LM to sort of show its work,
Speaker 2: break down the problem like we learned in school math
Speaker 2: pretty much. But here's a funny quirk. The book notes
Speaker 2: that sometimes for tasks where humans also tend to overthink
Speaker 2: and make mistakes, COKE can actually make the LLLM perform worse.
Speaker 2: A good reminder they aren't just scaled up human brains.
Speaker 1: Huh interesting?
Speaker 3: What else next?
Speaker 1: Up?
Speaker 2: And this is fundamental for making lllms useful with your
Speaker 2: data is retrieval augmented generation or OURG. This means providing
Speaker 2: relevant pieces of text also known as context, directly within
Speaker 2: the prompt. So if you want the LLLM to know
Speaker 2: about your company's latest internal reporter today's news, you use
Speaker 2: OURG to feed at that specific information alongside the question.
Speaker 1: Ah okay, so ori is how you give it knowledge
Speaker 1: it wasn't trained on precisely.
Speaker 2: And then for making lllms do things, there's tool calling.
Speaker 2: This lets you give the LM a list of external
Speaker 2: functions or calculator. Example, maybe a search engine, API, a
Speaker 2: weather service, whatever. You train it to recognize when it
Speaker 2: needs a tool and to signal it's intent.
Speaker 1: To use it, so it can decide I need to
Speaker 1: search for this or I need to calculate that exactly.
Speaker 2: And often the most powerful applications combine these techniques. Maybe
Speaker 2: use chain of thought to plan or ready to fetch
Speaker 2: relevant data, and then tool calling to perform a specific
Speaker 2: action or calculation based on that data. Oh and one
Speaker 2: more few shot prompting. This is where you give the
Speaker 2: LM just a small number of examples, like here's a question,
Speaker 2: here's the right kind of answer. It helps it learn
Speaker 2: new tasks or formats on the fly without full retraining,
Speaker 2: like showing it a few examples to get the hang
Speaker 2: of something new.
Speaker 1: Wow, Okay, that's a whole toolkit for interacting with them,
Speaker 1: and lang chain helps manage all of this.
Speaker 3: Yeah, that's the beauty of it.
Speaker 2: Lang chain was one of the earliest open source libraries
Speaker 2: to provide these core LM and prompting building blocks, and
Speaker 2: it's taken off massively. The community is huge, over seventy
Speaker 2: two thousand members, twenty eight million downloads a month.
Speaker 3: It's staggering.
Speaker 2: What lang chain does is offer these simple abstractions for
Speaker 2: all those tech niques we just discussed, zero shot solo
Speaker 2: t rrag tool calling fushot plus. It integrates seamlessly with
Speaker 2: all the major LLLM providers open Ai, Anthropic, Google, and
Speaker 2: popular open source models like Lama. This common interface is
Speaker 2: a really big deal. It means you can easily experiment,
Speaker 2: swap out different llms, and crucially avoid being locked into
Speaker 2: a single provider.
Speaker 3: That gives you a huge flexibility.
Speaker 1: That flexibility sounds key Okay, so this brings us to
Speaker 1: a really crucial challenge, especially for us the builders. If
Speaker 1: these alans are brilliant, but they fundamentally can't know everything.
Speaker 1: They weren't trained on my company's latest financials or yesterday's news,
Speaker 1: how do we stop them from just making stuff up,
Speaker 1: from hallucinating when we ask about that information.
Speaker 3: You've nailed the core problem.
Speaker 2: Just relying on the LM's pre train knowledge often isn't
Speaker 2: enough for real world apps, precisely because, like you said,
Speaker 2: they lack private data stuff not on the public Internet,
Speaker 2: and they lack knowledge of current events because of their
Speaker 2: knowledge cutoff date.
Speaker 3: When they don't have.
Speaker 2: The information they need, they tend to hallucinate, generating plausible
Speaker 2: sounding but incorrect or even totally fabricated answers.
Speaker 1: Which can be dangerous in a real application.
Speaker 2: Absolutely, and that's exactly where retrieval augmented generation are AG
Speaker 2: becomes essential. It's basically your defense mechanism against hallucination by
Speaker 2: providing the necessary context RAG.
Speaker 1: So walk us through how it actually works. How does
Speaker 1: it ground the LM in specific maybe private or very
Speaker 1: current information.
Speaker 2: Okay, So, our RAG is specifically designed to enhance the
Speaker 2: accuracy of outputs generated by llms by providing context from
Speaker 2: external sources. Meta AI actually coined the term, and their
Speaker 2: research found that RAG makes models more factual and specific.
Speaker 2: The whole process generally involves four key steps for getting
Speaker 2: your documents ready, sometimes called ingestion or indexing. First, you
Speaker 2: extract the text from whatever documents you have. Lang chain
Speaker 2: has helpers for this, like text loater for plain text files,
Speaker 2: or pd sloader for PDFs, and many others. Simple enough,
Speaker 2: the text out step one. Step two, you split that
Speaker 2: text into manageable chunks. This is really important because, as
Speaker 2: we mentioned, LM's have a context window, a limit on
Speaker 2: how much text they can look at in one go.
Speaker 2: Can't just feeding up five hundred page document, right, It's
Speaker 2: too big Exactly so, tools like lang teen's where cursive
Speaker 2: character text splitter, cleverly break the text down. It tries
Speaker 2: to split along natural boundaries first like paragraphs and sentences,
Speaker 2: then words. To keep things coherent, you can configure the
Speaker 2: chunk size and also add some chunk overlap, meaning consecutive
Speaker 2: chunks share a bit of text.
Speaker 1: Ah overlap helps maintain context across the breaks.
Speaker 2: Precisely, It's like making sure the end of one chapter
Speaker 2: flows smoothly into the start of the next third step.
Speaker 2: You convert these text chunks into numbers, specifically into embeddings.
Speaker 1: Embeddings. Okay, this sounds like where the magic happens.
Speaker 3: It kind of is.
Speaker 2: Think of an embedding as a long list of numbers
Speaker 2: a vector that represents the meaning of that text chunk.
Speaker 2: Now it's a lossy representation. You can't perfectly reconstruct the
Speaker 2: original words just from the numbers, like you can't get
Speaker 2: perfect ced quality back.
Speaker 3: From an MP three.
Speaker 1: But it captures the essence exactly.
Speaker 2: It captures the semantic essence, and this allows for math
Speaker 2: on words. This is a huge lead from older systems
Speaker 2: that just did keyword searching LM based embeddings or semantic
Speaker 2: embeddings understand meaning.
Speaker 1: Okay, this is fascinating. How do we teach a computer
Speaker 1: the difference between say, lion, pet and dog. I get
Speaker 1: the related but how does the computer quantify that? If
Speaker 1: we connect this to the bigger picture, this cosign similarity
Speaker 1: idea quantifying how close pet and dog are numerically versus lion.
Speaker 1: That seems powerful, But how does that number crunching actually
Speaker 1: enable better search?
Speaker 2: That's a fantastic question. Really gets to the heart of
Speaker 2: semantic search. Imagine all these words, or rather the concepts
Speaker 2: they represent, existing is points in some vast high dimensional space.
Speaker 2: The embedding vectors for pet and dog would literally be
Speaker 2: mapped closer together in this space. Then either would be
Speaker 2: to lion because they meanings are more related. Cosine similarity
Speaker 2: is just the mathematical tool we use to measure the
Speaker 2: angle or the closeness between these vectors. It gives a
Speaker 2: score usually between natus, one opposite meaning and one identical meaning.
Speaker 2: So pet and dog would have a cosine similarity score
Speaker 2: much closer to one than pet and lion.
Speaker 1: Ah. Okay, so similarity means closer in this meaning space exactly.
Speaker 2: And this ability to turn text into embeddings that capture
Speaker 2: deep meaning lets us search based on concepts, not just keywords.
Speaker 2: You could search for happy house animal and the system
Speaker 2: could find documents talking about joyful puppies or content cats.
Speaker 2: Even if the exact words happy house or animal aren't there,
Speaker 2: it understands the underlying meaning is similar.
Speaker 1: That's incredibly powerful search. Okay, so we've extracted, split, and
Speaker 1: embedded the text. What's the final step.
Speaker 2: The fourth step is to store these embeddings in a
Speaker 2: vector store. Think of this as a specialized database designed
Speaker 2: to store these numerical vectors and perform those complex similarity
Speaker 2: calculations like cosine similarity really efficiently and quickly. There are
Speaker 2: lots of options, open source ones like pg vector, an
Speaker 2: extension for post cresscool, dedicated databases like wev eight or
Speaker 2: pine Cone, or cloud services. When you, the user ask
Speaker 2: a question, your question is also converted into an embedding vector.
Speaker 2: The vector store then rapidly finds the stored embeddings and
Speaker 2: their corresponding text chunks that are most similar mathematically to
Speaker 2: your query embedding. Those relevant chunks are then retrieved and
Speaker 2: passed to the LLM along with your original.
Speaker 1: Question, giving it the specific context it needs to answer
Speaker 1: accurate precisely.
Speaker 2: And lane Chain also provides tools like its indexing API
Speaker 2: and record manager to help keep this vector store up
Speaker 2: to date. As your source documents change, you can efficiently
Speaker 2: track those changes, add new embeddings, remove old ones, and
Speaker 2: avoid costly reprocessing of unchanged documents, keeping the knowledge current.
Speaker 1: Okay, that makes sense. We've got the basic arget pipeline down,
Speaker 1: index the data, retrieve relevant chunks, give them to the LLM.
Speaker 1: But I imagine building something truly production ready involves more nuance.
Speaker 1: What are the common challenges and how do we refine
Speaker 1: that search for knowledge to be even more accurate and robust?
Speaker 2: Yeah, moving from a basic a RAGI demo to production
Speaker 2: definitely introduces complexity. Users ask questions in all sorts of ways,
Speaker 2: sometimes ambiguously. Your data might live in multiple different places,
Speaker 2: and you often need to translate that natural language question
Speaker 2: into something more structured for retrieval. So we need more
Speaker 2: advanced strategies.
Speaker 1: Okay, what are they?
Speaker 2: The book highlights three main categories of strategy. The first
Speaker 2: is query transformation. The idea here is to modify the
Speaker 2: user's input before you even search to improve the chances
Speaker 2: of finding the best documents.
Speaker 1: Ah, like cleaning up the question first exactly.
Speaker 2: One technique is rewrite retrieve read. Here, you actually use
Speaker 2: another LM call first just to rewrite the user's potentially
Speaker 2: vague or conversational query into a clearer, more focused search query.
Speaker 2: Then you use that rewritten query for the retrieval step.
Speaker 1: Smart like having an assistant clarify your question before searching.
Speaker 1: Does it add much delay?
Speaker 2: Yeah, it's a little bit of latency. Yeah, because it's
Speaker 2: an extra LM call, but often the improvement in retrieval
Speaker 2: quality is worth it. Another transformation technique is multi query retrieval.
Speaker 2: Instead of just one query, you have the LMM generate
Speaker 2: multiple versions of the given user question, maybe from slightly
Speaker 2: different angles or using different keywords.
Speaker 1: Oh interesting, why do that?
Speaker 2: It's great for complex questions that might need information from
Speaker 2: multiple perspectives. You run retrievals for all those generated queries
Speaker 2: in parallel, then combine the unique documents found. It casts
Speaker 2: a wider net, reducing the chance you miss something important.
Speaker 2: Building on that is RAG fusion. It starts like multiquery,
Speaker 2: generating multiple queries and retrieving results for each, but then
Speaker 2: it has a crucial final re ranking step using something
Speaker 2: called the reciprocal rank of fusion.
Speaker 3: RF algorithm.
Speaker 1: Sounds technical.
Speaker 2: It's a clever way to combine the rankings from all
Speaker 2: the different searches. Documents that consistently rank highly across multiple
Speaker 2: queries get boosted to the very top. Really effective at
Speaker 2: finding the most relevant stuff while also broadening discovery.
Speaker 1: Okay, so RF aggregates the wisdom of multiple searches. Cool
Speaker 1: any other transformation tricks?
Speaker 2: One more interesting one is hypothetical document embeddings or Heidi
Speaker 2: this kind of counterintuitive. Instead of searching with the user's query,
Speaker 2: you first have an LM create a hypothetical document that
Speaker 2: would be a perfect answer to the query.
Speaker 1: Wait, it makes up an answer first.
Speaker 3: Yeah.
Speaker 2: The intuition is that this generated ideal answer, even though
Speaker 2: it's hypothetical, is often semantically more similar to the actual
Speaker 2: relevant documents than the original maybe short or ambiguous user query.
Speaker 2: So you embed this hypothetical answer and use that embedding
Speaker 2: for the similarity search.
Speaker 1: Huh, that's sliver. Using an ideal answer is a better
Speaker 1: search query? Okay, so that's query transformation. What's the second strategy?
Speaker 2: The second strategy is query routing. This tackles the problem
Speaker 2: you mentioned earlier. What if your data lives in different places.
Speaker 2: Maybe you have Python docks in one vector store and
Speaker 2: JavaScript docs in another.
Speaker 1: Right, how do you send the query to the right place?
Speaker 2: That's exactly what quer routing does, forward a user's query
Speaker 2: to the relevant data source. There are a couple of
Speaker 2: ways logical routing uses an LLM to make the decision.
Speaker 2: You give the LM descriptions of your available data sources,
Speaker 2: like this index contains technical documentation for Python and Based
Speaker 2: on the user's query, the LM picks which of the
Speaker 2: available indexes to use. Lang chain helps ensure the LM
Speaker 2: outputs its choice in a structured way your application can understand.
Speaker 1: So the LLM acts like a switchboard operator kind of.
Speaker 3: Yeah.
Speaker 2: Alternatively, there's semantic routing.
Speaker 3: Here.
Speaker 2: You embed the descriptions of your data sources themselves. Then
Speaker 2: you compare the user's query embedding to these description embeddings.
Speaker 2: The closest match indicates the most relevant data source. This
Speaker 2: is more dynamic, doesn't require an LLLM call for every
Speaker 2: routing decision.
Speaker 1: Okay, route it logically or semantically makes sense. What's the
Speaker 1: third major strategy?
Speaker 2: The third is query construction. This is about transforming a
Speaker 2: natural language query into the query language of the database
Speaker 2: or data source you were interacting with. It goes beyond
Speaker 2: just finding similar text chunks. Oh so, well, maybe you
Speaker 2: need to combine semantic search with traditional database filters. Text
Speaker 2: to metadata filter is a technique where the LLM extracts
Speaker 2: structured information like a date, a category, a price range
Speaker 2: directly from the user's natural language query.
Speaker 1: Ah so, if I ask for sci fi movies from
Speaker 1: the eighties, it pulls out sci fi for semantic search
Speaker 1: and nineteen eighties as a metadata filter exactly.
Speaker 2: It lets you combine the power of semantic understanding with
Speaker 2: the precision of structured filters. Another big one here is
Speaker 2: text to seql. This involves having the LM translate a
Speaker 2: natural language question like what were our total sales in
Speaker 2: Q three directly into an executable SQL query to run
Speaker 2: against a traditional relational database.
Speaker 1: Wow, that's powerful. How do you make that reliable? SQL?
Speaker 1: Can be tricky?
Speaker 2: It requires careful setup. You usually need to provide the
Speaker 2: LLM with a description of the database scheme like the
Speaker 2: create table statements, maybe some example rows, and often some
Speaker 2: few shot examples of natural language questions paired with their
Speaker 2: correct SQL queries.
Speaker 3: To guide it.
Speaker 1: Got it? So, text to sqel translates language to database code.
Speaker 2: If we connect this text to SQL capability to the
Speaker 2: bigger picture. Though, while it's incredibly powerful, letting an LEBLEM
Speaker 2: generate SQL queries directly from potentially untrusted user input is
Speaker 2: one of the riskiest things you can do in a
Speaker 2: production application. Ah.
Speaker 1: Security implications, Yeah, I can see that.
Speaker 2: Absolutely. This raise is a really important question around safety.
Speaker 2: You must implement critical security measures, things like ensuring the
Speaker 2: database connection has read only permissions, strictly limiting access to
Speaker 2: only the necessary tables, maybe even views, and definitely adding
Speaker 2: query timeouts to prevent denial of service attacks or runaway queries.
Speaker 2: It's a capability that demands extreme caution and robust safeguards.
Speaker 1: Absolutely crucial point. Okay, this is getting really interesting, especially
Speaker 1: for building interactive apps. How do we tackle the fact
Speaker 1: that lllms are inherently forgetful. How do we give them
Speaker 1: memory to actually hold a conversation, especially as things get
Speaker 1: more complex.
Speaker 2: Yeah, you've hit on a fundamental aspect. Llms are stateless.
Speaker 2: Every time you interact with them. It's like a fresh start.
Speaker 2: There's no memory of the prior prompt or model response
Speaker 2: built in. It's like talking to someone who forgets everything
Speaker 2: you just said every.
Speaker 1: Single turn, which isn't great for a chatbot.
Speaker 3: Not at all.
Speaker 2: The simplest way to build memory is just to literally
Speaker 2: store the history of the chatter, all the user messages
Speaker 2: and assistant responses as a list, and then include that
Speaker 2: entire list in the prompt for the next turn.
Speaker 1: Okay, just stuff the whole conversation history back in.
Speaker 2: Basically, Yeah, yeah, but you can imagine at scale. That
Speaker 2: gets tricky. How do you update that history reliably? How
Speaker 2: do you manage state? When you have multiple things happening,
Speaker 2: maybe multiple actors or steps in your application? It gets
Speaker 2: complicated fast. And that's precisely where langgraf enters the picture.
Speaker 2: Lang graph acts as the coordination layer these more complex,
Speaker 2: multi step, potentially multi actor applications. It's what allows them
Speaker 2: to remember state and coordinate actions over time.
Speaker 1: Lang grap Okay, how does it work? Is it like
Speaker 1: a state machine?
Speaker 2: You think of it kind of like designing a flow
Speaker 2: chart for your AI app. It is three core components. First,
Speaker 2: there's the state. This is the shared data that evolves
Speaker 2: over the course of the application run. It can include
Speaker 2: the chat history, intermediate results, anything the application needs to remember. Second,
Speaker 2: you have nodes. These are the individual steps or functions
Speaker 2: in your flow chart. A node might be a call
Speaker 2: to an LM, a call to a tool like our
Speaker 2: calculator or search engine, or just some regular Python code
Speaker 2: that processes the state. And Third, you have edges. These
Speaker 2: are the connections between the nodes, determining the flow of execution.
Speaker 2: Edges can be fixed like always go from no day
Speaker 2: to node B, or they.
Speaker 1: Can be conditional conditional edges, meaning.
Speaker 2: Meaning the next step depends on the current state. Often,
Speaker 2: an LM in one node might decide which node to
Speaker 2: go to next, making the flow dynamic. A huge benefit
Speaker 2: of lang graph is its built in persistence. It uses
Speaker 2: something called a checkpointer. You can think of it like
Speaker 2: an auto save.
Speaker 1: Function AH, so it saves the state automatically exactly.
Speaker 2: It saves the current state after each step. This means
Speaker 2: that every invocation after the first doesn't start from blank slate.
Speaker 2: If the app crashes or the user comes back later,
Speaker 2: it can pick up right where it left off from
Speaker 2: the last save state. Really important for long running or
Speaker 2: stateful interactions.
Speaker 1: That's huge for usability. What about that growing chat history though,
Speaker 1: Does lang graph help manage that so you don't overload
Speaker 1: the LLM?
Speaker 3: Yes? Absolutely.
Speaker 2: While lang graph manages the overall state persistence, you still
Speaker 2: use lane chain's utilities within your nodes to manage the
Speaker 2: specific chat history part of the state before passing.
Speaker 3: It to an LLM.
Speaker 2: You can indeligently filter messages, maybe keeping only summaries or
Speaker 2: key turns, trim the history based on the number of
Speaker 2: messages or total tokens, or even merge older parts of
Speaker 2: the conversation into a concise summary, all designed to keep
Speaker 2: the context relevant without ex eating.
Speaker 3: Those M limits.
Speaker 1: Okay, so lang graph orchestrates the flow in memory, and
Speaker 1: lang chain helps manage the conversation content. That makes sense.
Speaker 1: So we've gone from just calling an LLLM once to
Speaker 1: chains and now to the state ful lang graph applications.
Speaker 1: How should we think about the different levels of complexity
Speaker 1: and I guess intelligence in these systems.
Speaker 2: That's a great way to frame it. We can think
Speaker 2: about a progression of cognitive architectures moving towards more sophisticated applications,
Speaker 2: and as we move up this ladder, we constantly grapple
Speaker 2: with that fundamental trade off we mentioned agency, the lmm's
Speaker 2: capacity to act autonomously, and reliability the degree to which
Speaker 2: we can trust its outputs. More autonomy often means less predictability.
Speaker 1: Right the agency versus reliability balance.
Speaker 3: So progression looks something like this.
Speaker 2: At the base, you have a simple LM call, one input,
Speaker 2: one output, like asking get to summarize text. Simple, usually
Speaker 2: reliable for that specific task. Next level up is a chain.
Speaker 2: This involves multiple LELLM calls or calls to tools executed
Speaker 2: in a pre defined fixed sequence. Example, step one LLLLM
Speaker 2: generates a SEQL query. Step two different M explains that
Speaker 2: query in plain English. The sequence never changes.
Speaker 1: Okay, fixed steps like an assembly.
Speaker 2: Line pretty much. Then it gets more dynamic with the router. Here,
Speaker 2: an LOM decides the sequence of steps. At runtime, it
Speaker 2: chooses which pre defined path to take based on the input.
Speaker 2: Like an earlier example, if it's a medical question, rode
Speaker 2: to the medical index, if insurance route to the FAQ index.
Speaker 2: The LOLLM makes a choice, but the possible paths are
Speaker 2: still pre defined.
Speaker 1: So it adds a decision point. And beyond writers, we
Speaker 1: finally get to agents. That seems to be the buzzword
Speaker 1: everyone's excited about.
Speaker 2: Exactly an agent is quite simply something that acts. What
Speaker 2: makes agent architectures unique and powerful is that they use
Speaker 2: an LLLM driven loop for control. The LM isn't just
Speaker 2: executing pre defined steps. It's deciding what to do next
Speaker 2: based on the result of its previous actions, and critically,
Speaker 2: it decides when to stop. The most common pattern here
Speaker 2: is the plan do loop, often called the react to
Speaker 2: architecture reasoning plus acting plan do loop.
Speaker 1: How does that work in practice?
Speaker 2: Imagine an agent needs to answer a complex question that requires, say,
Speaker 2: searching the web and then doing a calculation. Step one,
Speaker 2: Them gets the question, observes. Step two, it thinks, for reasons, okay,
Speaker 2: answer this, I first need to search for X. Step three,
Speaker 2: it plans an action, call the search tool with query X.
Speaker 2: Step four, the system executes, the search tool does step five.
Speaker 2: Them gets the search results back, observes again. Step six,
Speaker 2: It thinks again, okay, based on these results, now we.
Speaker 3: Need to calculate why using the calculator tool.
Speaker 2: Step seven, it plans the next action, call the calculator
Speaker 2: with inputs A and B. Step eight, the system executes
Speaker 2: the calculator does. Step nine. The LOM gets the calculation
Speaker 2: result observes.
Speaker 3: Step ten.
Speaker 2: It thinks, one last time, okay, now I have all
Speaker 2: the pieces I can formulate the final answer. It decides
Speaker 2: the loop is finished. Step eleven, it outputs the final
Speaker 2: answer to the user. See how the LOM is driving
Speaker 2: the whole process, deciding which tool to use one and
Speaker 2: ultimately deciding when it's done. That iterative self directed loop
Speaker 2: is the core of an agent. Plank graph is perfect
Speaker 2: for implementing these loops using its nodes for the LLM
Speaker 2: calls and tool executions and conditional edges to route the
Speaker 2: flow based on the LM's decisions.
Speaker 1: Wow, okay, that really clarifies it. The LLM is in
Speaker 1: the driver's seat, choosing actions and deciding when the job
Speaker 1: is done. That's a big step up in autonomy. Can
Speaker 1: we make these agents even smarter?
Speaker 2: Definitely? There are enhancements. For instance, you might design the
Speaker 2: agent to always call a tool first, maybe always starting
Speaker 2: with a search to ensure its reasoning is grounded in
Speaker 2: current information before it even tries to answer. Another challenge
Speaker 2: arise is when you have many possible tools, how does
Speaker 2: the agent pick the right one? You can actually use
Speaker 2: our rag on the tool descriptions. Store descriptions of all
Speaker 2: your tools in a vector store, and when the agent
Speaker 2: decides it needs a tool, it first does a semantic
Speaker 2: search over the tool descriptions to find the most relevant
Speaker 2: one for the.
Speaker 1: Task at hand, using our rig to help the agent
Speaker 1: choose its own tools. That's pretty meta. This sounds incredibly powerful,
Speaker 1: giving models the ability to genuinely tackle multi step problems.
Speaker 1: Can they go even further?
Speaker 3: Like?
Speaker 1: Can agents learn or work together or maybe even critique
Speaker 1: their own work?
Speaker 3: Yes? Absolutely, to all of those.
Speaker 2: One really powerful extension is reflection or self critique. This
Speaker 2: involves setting up a kind of loop, often using multiple
Speaker 2: LLM calls that mimics how humans create and refine things.
Speaker 2: You might have a creator prompt that generates a first
Speaker 2: draft of something, say in an essay. Then you have a
Speaker 2: separate revisor prompt with the LLLM critiques that draft based
Speaker 2: on specific criteria like clarity, tone, fascial accuracy. Finally, the
Speaker 2: original lam or another one revises the draft based on that.
Speaker 1: Critique, so it acts as its own editor exactly.
Speaker 2: It allows the LM to refine its output, maybe catch
Speaker 2: errors or improved style. You can eat something of the
Speaker 2: revisor LLM to adopt a different persona while critiquing, like
Speaker 2: asking it to review the essay from the perspective of
Speaker 2: a skeptical historian. This iterative refinement can significately boost quality.
Speaker 1: That's amazing. What about teamwork? Can you have multiple agents collaborating?
Speaker 2: Yes, for really complex problems that might be too much
Speaker 2: for a single agent, maybe requiring too many different tools
Speaker 2: or too much context, you can use multi agent architectures.
Speaker 2: You literally build teams of LVM agents that work together.
Speaker 2: There are different ways to coordinate these teams, but a
Speaker 2: practical approach highlighted in the book is the supervisor architecture.
Speaker 2: Here you have a central supervisor agent often an element self,
Speaker 2: whose job is to manage the workflow based on the
Speaker 2: overall goal and the current state. The supervisor decides which
Speaker 2: agent or agents should be called next. It wrote tasks
Speaker 2: to specialize subagents. Maybe one agent is good at research,
Speaker 2: another at writing code, another at summarizing. Their progress and
Speaker 2: results are often shared in a central place, like a
Speaker 2: list of messages in the lang graft state, allowing them
Speaker 2: to build on each other's work. It enables true collaborative
Speaker 2: problem solving among AI agents.
Speaker 1: So what does this all mean? We're talking about building
Speaker 1: digital teams that can think, plan, act, reflect on their actions,
Speaker 1: and even critique their own work. It feels like this
Speaker 1: genuinely expands the kinds of problems we can even attempt
Speaker 1: to solve with AI.
Speaker 2: It really does, and it brings us right back to
Speaker 2: the fundamental tension we keep mentioning the trade off between
Speaker 2: agency and reliability. As these agents become more autonomous, controlling
Speaker 2: them and trusting their output becomes even more critical. If
Speaker 2: we zoom out again, and connect this to the bigger picture.
Speaker 2: You can visualize this trade off as a kind of
Speaker 2: frontier on a graph. You want to push that frontier outwards,
Speaker 2: achieve more agency for the same level of reliability, or
Speaker 2: achieve higher reliability for the same level of agency. Pushing
Speaker 2: this frontier is key to building production ready applications people
Speaker 2: can actually depend on.
Speaker 1: Right, trust is paramount, So specifically, how do we improve
Speaker 1: the actual user experience and maybe more importantly, the reliability
Speaker 1: and control in these powerful, sometimes complex, agentic systems.
Speaker 2: Great question. There are several vital techniques discussed in the book.
Speaker 2: First off, managing latency p reception with streaming an intermediate output.
Speaker 2: LLM calls, especially in complex agent loops, can take time.
Speaker 2: A few seconds of waiting can feel long to a user,
Speaker 2: so communicating progress makes that higher latency more palatable. This
Speaker 2: includes dreaming the llm's final output token by token so
Speaker 2: the text appears gradually like someone typing.
Speaker 1: Makes it feel more responsive exactly.
Speaker 2: It also includes showing intermediate steps maybe messages like okay,
Speaker 2: searching for X or now calculating Y, so the user
Speaker 2: sees the agent as working. Second, and absolutely critical for
Speaker 2: reliability is ensuring structured output. You often need the LLM
Speaker 2: to return information in a specific predictable format like Jason,
Speaker 2: not just freeform text. Linke dams with structure output method
Speaker 2: is designed for this. It helps reduce variants and ensures
Speaker 2: downstream systems can reliably parse and use the llm's output.
Speaker 2: Using a low temperature setting often helps here too.
Speaker 1: So you get predictable data structures back, not just a
Speaker 1: chatty response precisely.
Speaker 2: Third, especially for high agency applications, you need human in
Speaker 2: the loop controls. These give essential oversight to the end user,
Speaker 2: allowing intervention and correction. One control is interrupt The user
Speaker 2: should be able to manually stop an ongoing agent process
Speaker 2: at any time. Ideally the state is saved, so they
Speaker 2: can choose to resume later, restart, or just abandon it.
Speaker 2: If the agent's going off track. A panic button basically
Speaker 2: kind of another is authorized. The application pauses before performing
Speaker 2: a potentially critical or irreversible action, maybe sending an email,
Speaker 2: making a purchase, modifying a file, and ask the user
Speaker 2: for explicit confirmation is it okay to do this?
Speaker 1: Essential for safety absolutely.
Speaker 2: And then there's the ability to fork and replay history.
Speaker 2: This allows the user to effectively go back in time
Speaker 2: to an earlier point in the conversation or workflow state,
Speaker 2: and then start a new branch from there, trying a
Speaker 2: different approach. It's fantastic for experimentation, debugging, and recovering from
Speaker 2: errors without starting over completely.
Speaker 1: Those controls sound invaluable for making these powerful systems usable
Speaker 1: end safe in the real world. Okay, what about a
Speaker 1: common scenario. Llms can be a bit slow. What happens
Speaker 1: if a user sends a new message while the agent
Speaker 1: is still thinking about the previous one. How does systems
Speaker 1: handle that concurrency?
Speaker 2: That's the challenge of multitasking lllms. Handling concurrent inputs lms
Speaker 2: are often quite slow compared to traditional software responses. Users
Speaker 2: will send follow up messages or new requests before the
Speaker 2: first one is finished. There are different strategies. The simplest
Speaker 2: is just to refuse concurrent input maybe disable the input
Speaker 2: box while processing.
Speaker 1: Not very user friendly, Yeah, frustrating.
Speaker 3: Right.
Speaker 2: You can handle each input independently in a new thread,
Speaker 2: but that might lose conversational context. You could queue inputs,
Speaker 2: processing them one after another, or you can interrupt the
Speaker 2: current run to prioritize the new input, either abandoning the
Speaker 2: old one or trying to save its partial state. This
Speaker 2: raises an interesting question. If an LLM is mid computation
Speaker 2: and you send another message, how should it respond intelligently?
Speaker 2: The book mentions in advanced sategy called.
Speaker 3: Fork and merge.
Speaker 1: Fork and merge.
Speaker 2: Yeah, The idea is the system temporarily forks the agent's
Speaker 2: current state. When new input arrives, it processes the new
Speaker 2: input in parallel, perhaps starting from that fork state. Then
Speaker 2: somehow it intelligently merges the results or final states from
Speaker 2: both the original computation and the new parallel one. It's
Speaker 2: complex to implement correctly, requiring careful steat management, but it
Speaker 2: could allow for very fluid interruption tolerant interactions.
Speaker 1: Wow. Okay, that sounds complex but powerful for smooth interaction.
Speaker 3: Yeah.
Speaker 1: So we've designed the core logic, we've added memory with
Speaker 1: lang graph, We've considered reliability and UX controls. Now, how
Speaker 1: do we actually get this thing out of our development
Speaker 1: environment and deployed for external users, making sure it's stable
Speaker 1: and can handle real.
Speaker 2: Traffic Right the production leak? This is where things get serious.
Speaker 2: The book highlights lang graph platform as a solution here.
Speaker 2: It's essentially a managed service for deploying and hosting lang
Speaker 2: graph agents at scale. Its goal is to handle the
Speaker 2: operational headachesction. It provides things like horizontally scaling task queues
Speaker 2: and servers to handle many concurrent users, plus a robust
Speaker 2: postgress checkpointer for efficiently storing potentially large states and conversation threads.
Speaker 2: The aim is fault tolerant scalability, so.
Speaker 1: It takes care of the scaling and reliability infrastructure.
Speaker 3: That's the idea.
Speaker 2: Now, before you deploy there, you'll need a few prerequisite
Speaker 2: setup your apikeys from your LLM provider like OpenAI, a
Speaker 2: configured vector store if you're using RWI. The book mentioned
Speaker 2: Superbase with its pg vector extension is a good option,
Speaker 2: and you'll need a langsmith account because lang graft platform
Speaker 2: is tightly integrated with langsmith for monitoring and debugging.
Speaker 1: Langsmith. What's that?
Speaker 2: Langsmith is another part of the lang sching ecosystem focus
Speaker 2: specifically on observability, debugging, testing and monitoring for LLLM applications.
Speaker 2: It's pretty crucial for the whole life cycle.
Speaker 1: Okay, got it, So you set up prerequisites, then how
Speaker 1: do you deploy to lang graph platform.
Speaker 2: The process is designed to be fairly straightforward. You typically
Speaker 2: define your lang graph application structure in a configuration file,
Speaker 2: often the langgraph dot json. You can test it locally
Speaker 2: using the lang graph command line interface the CLI with
Speaker 2: a command like lang grafh dev, and then deployment to
Speaker 2: the managed platform is often done via one click submissions
Speaker 2: directly from the langsmith user interface.
Speaker 1: Okay, seems streamlined, but deployment isn't the end, right. We
Speaker 1: keep coming back to the fact that llms are non
Speaker 1: deterministic and prone to hallucination. How do we build and
Speaker 1: maintain trust after launch? How does continuous improvement work in
Speaker 1: this AI world?
Speaker 2: This is absolutely critical. Deployment is just the beginning of
Speaker 2: the journey. You need that continuous improvement cycle. Design, test
Speaker 2: data is deployed on monitor and fixed JAD redesign. Even
Speaker 2: in the design stage, you can build in defensiveness like
Speaker 2: that self corrective our gag idea we touched on earlier.
Speaker 2: You can have an LLLM within your agent's flow whose
Speaker 2: job is to grade retrieval relevance. Did we find good
Speaker 2: documents and check the answer for hallucinations before showing it
Speaker 2: to the user.
Speaker 1: The LM double checks itself yeah.
Speaker 2: And if it decides the retrieval was poor or the
Speaker 2: answer looks suspicious, it could trigger a fallback. Maybe try
Speaker 2: a web search instead, or ask the user for clarification.
Speaker 2: Building self correction right into the design. Then comes pre
Speaker 2: production testing. This is all about measuring accuracy, latency, cost,
Speaker 2: whatever metrics matter to you before you expose the app
Speaker 2: to real users. For this, you need good data sets.
Speaker 1: Where do those data sets come from?
Speaker 2: Several sources. You can have manually curated examples humans carefully
Speaker 2: writing good questions and ideal answers. You can use application
Speaker 2: logs from early internal testing or beta users, or you
Speaker 2: can even generate synthetic data using other lolms to create
Speaker 2: diverse examples of inputs and outputs. Langsmith actually has tools
Speaker 2: specifically for creating and managing these test data sets. Once
Speaker 2: you have data, you need evaluation criteria. You compare your
Speaker 2: app's output against some ground truth references.
Speaker 3: How do you evaluate? You can use human evaluators people.
Speaker 2: Giving quolitative feedback on nuance, tone correctness, very valuable, but
Speaker 2: slow and expensive. You can use heuristic evaluators basically simple
Speaker 2: hard code of checks like does the output contain the
Speaker 2: specific keyword? Is a below a certain length? Quick, but
Speaker 2: limited increasingly popular lmms is a judge evaluators. Here, you
Speaker 2: use another LM, give it a real quick and ask
Speaker 2: it to score or critique your application's output based.
Speaker 3: On that rubric.
Speaker 1: Using an LM to judge another LLLM exactly.
Speaker 2: And what's really fascinating here, Lanksmith has this clever feedback loop.
Speaker 2: If a human corrects or disagrees with the LM as
Speaker 2: a judge's assessment, lank Smith captures that correction and automatically
Speaker 2: turns it into a fu shot example that gets added
Speaker 2: back into the judge's prompt for future evaluations.
Speaker 1: Wow, so the judging LLM actually learns from human corrections
Speaker 1: over time.
Speaker 2: Precisely, it helps the automated evaluation along better with human preferences,
Speaker 2: reducing the need for constant manual prompt tweaking for the judge.
Speaker 2: It's a really smart self improving mechanism. You also need
Speaker 2: rigorous regression testing. As you update your code or even
Speaker 2: the underlying LLLM models change model drift, you need to
Speaker 2: constantly rerun your tests to prevent regression and sure you
Speaker 2: haven't accidentally made things worse. Langsmith's comparison view is designed
Speaker 2: to help spot these performance changes over time, and for
Speaker 2: complex agents, evaluation needs to happen on multiple levels the
Speaker 2: final response, but also individuals single step decisions like did
Speaker 2: it pick the right tool, and even the entire trajectory,
Speaker 2: the sequence of actions it took.
Speaker 1: Okay, that's a lot of testing before launch.
Speaker 2: What about after production? Monitoring is crucial for catching bugs
Speaker 2: and weird edge cases that only emerge with real users
Speaker 2: and real world data. Lang Smith is key here again
Speaker 2: providing tracing to track exactly what happened inside your agent
Speaker 2: when it error occurred or user gave bad feedback. You
Speaker 2: need mechanisms for collecting feedback and production, maybe simple thumbs
Speaker 2: updown buttons, annotation ques where users or internal teams can
Speaker 2: flag issues, or even running those LMM as a judge
Speaker 2: evaluators on live traffic samples. You can also implement classification
Speaker 2: and tagging on inputs and outputs, checking for things like toxicity,
Speaker 2: personally identifiable information, or even trying to detect prompt injection attacks.
Speaker 2: These act as safety guardrails and a really important practical tip.
Speaker 2: Release your app in phases. Start with a small group
Speaker 2: of Beata users, gather feedback, fix issues, then gradually expand
Speaker 2: the rollout. Don't just flip the switch for everyone on
Speaker 2: day one.
Speaker 1: So what this all really means, this continuous cycle of design, testing, evaluation, monitoring, fixing.
Speaker 1: It's not just about squashing bugs, is it. It feels
Speaker 1: like it's about systematically building confidence and trust in these
Speaker 1: incredibly powerful but inherently probabilistic systems. It's about making them
Speaker 1: reliable enough for the real world. Okay, thinking bigger picture, now,
Speaker 1: let's unpack this idea. Llms are amazing because they're so intuitive, right,
Speaker 1: They often understand what we mean, even with typos or
Speaker 1: slightly vague questions. That's fantastic. But that same flexibility means
Speaker 1: their output isn't always perfectly predictable. It can be slightly off,
Speaker 1: and that challenges our traditional software interfaces, which we usually
Speaker 1: build expecting very precise, deterministic results. How is this fundamental
Speaker 1: difference going to change the way we actually interact with software.
Speaker 3: That's a really profound question. You're right.
Speaker 2: Traditionally UIs think Microsoft Word, Figma spreadsheets. They have fixed
Speaker 2: tool pallettes, predictable menus canvases where actions have precise, repeatable
Speaker 2: outcomes because the underlying logic is deterministic LLLM powered applications
Speaker 2: are just different. They are more forgiving of messy input,
Speaker 2: which is great, but their output does have that inherent variability.
Speaker 2: This mismatch is pushing us to think about new interaction patterns.
Speaker 2: New UIs designed for this LLM native world. The book
Speaker 2: outlines three really interesting emerging patterns. The first and probably
Speaker 2: the easiest lift to integrate into existing apps is the
Speaker 2: interactive chatbot. Think of this as an AI sidekick living
Speaker 2: within your application, like get up copilot, chat within your
Speaker 2: code editor, or a similar chat interface within a design
Speaker 2: tool or document editor. This chatbi can see the main
Speaker 2: application content, the code, the design, the document and interact.
Speaker 1: With it so you can talk to it about the
Speaker 1: thing you're working.
Speaker 2: On, exactly, explain this code, suggest a different layout. Summarize
Speaker 2: this section. It's conversational collaboration. The key components are a
Speaker 2: good dialogue tune chat model obviously, conversation history, streaming outputs
Speaker 2: so it feels responsive, tool calling so the chatbot can
Speaker 2: actually invoke application functions, change the font size, refactor this code,
Speaker 2: and probably human in the loop controls for safety.
Speaker 1: Okay, the AI sidekick makes sense. What's the next pattern?
Speaker 2: The second pattern pushes the collaboration idea further collaborative editing
Speaker 2: with lms. Here the LLM agent isn't just a sidekick
Speaker 2: you talked to. It becomes one of those users contributing
Speaker 2: to this shared document or shared state, right alongside human collaborators.
Speaker 2: Think Google docs, but one of the cursors belongs to
Speaker 2: an AI.
Speaker 1: Whoa in AI is a real time teammate.
Speaker 2: Potentially, Yes, what's fascinating here is how this could work.
Speaker 2: Maybe the LM acts as an asynchronous drafter, pharing sections
Speaker 2: for you overnight, or maybe it's an always on copilot
Speaker 2: subjecting improvements or cleaning up formatting in real time. If
Speaker 2: we connect this to the bigger picture, it raises really
Speaker 2: important questions about how we design systems Where human edits
Speaker 2: and AI edits merge seamlessly, how do you handle conflicts
Speaker 2: whose changes take precedence?
Speaker 1: Yeah, the merging in conflict resolution sounds tricky.
Speaker 3: Definitely.
Speaker 2: Key components here involve managing that shared state carefully, maybe
Speaker 2: using sophisticated techniques like conflict free replicated data types crdts,
Speaker 2: or just robust merging logic. You need task managers, ways
Speaker 2: to handle concurrency, and definitely a good under.
Speaker 1: Redos stack an AI teammate directly editing alongside you. That's
Speaker 1: a huge paradigm shift. What's the third emerging pattern?
Speaker 2: The third and perhaps the most futuristic feeling is ambient computing.
Speaker 2: This is where the LLM is continuously doing some kind
Speaker 2: of work in the background while you, the user, are
Speaker 2: presumably doing something else.
Speaker 3: Entirely.
Speaker 2: It's not waiting for your explicit command, proactively working on your.
Speaker 1: Behalf as silent, always on assistant kind of Think about
Speaker 1: how LLM reasoning could transform this.
Speaker 2: Old ambient computing often required setting up lots of manual rules.
Speaker 2: If I get an email from X, notify me tedious.
Speaker 2: The fascinating question now is can elms use their understanding
Speaker 2: and reasoning to proactively identify what's genuinely interesting or important
Speaker 2: to you without needing endless configuration.
Speaker 1: So it learns what matters to me and surfaces just that.
Speaker 2: That's the potential. Key components here would include triggers detecting
Speaker 2: new information like emails, news, calendar, updates, long term memory
Speaker 2: to build context about you and your priorities, reflection, the
Speaker 2: agent actively learning and updating its internal model of what
Speaker 2: you find interesting, and crucially summarized output. It doesn't bombard you.
Speaker 2: It intelligently summarizes its findings and surfaces only the noteworthy stuff.
Speaker 2: It needs a task manager too, to keep track of
Speaker 2: his background processes.
Speaker 1: So what does this all really mean for how we'll
Speaker 1: interact with tech? Could lms truly become these quiet, proactive assistants,
Speaker 1: maybe to drafting replies, summarizing reports, alerting us to opportunities
Speaker 1: all happening in the background without constant manual setup. That
Speaker 1: feels like a fundamentally different way to experience software.
Speaker 2: It really does point towards a potential future where software
Speaker 2: is less a collection of static tools we actively wield
Speaker 2: and more of an intelligent environment that it anticipates and assists.
Speaker 1: We have covered an incredible amount of ground today, haven't we.
Speaker 1: It feels like a whirlwind.
Speaker 3: Tourth It really does.
Speaker 1: From the absolute fundamentals of llms, how they predict text,
Speaker 1: what tokens are, and how prompting is our way to
Speaker 1: steer them, then diving deep into retrieval augmented generation our RAG,
Speaker 1: that crucial technique for grounding them in real world specific
Speaker 1: data to fight hallucinations.
Speaker 2: Yeah, and then moving into land graph enabling these complex
Speaker 2: multi step agents that can actually remember conversations, plan sequences
Speaker 2: of actions using tools, and even reflect and critique their own.
Speaker 1: Out puts exactly. And then tackling the really practical side
Speaker 1: making these things production ready, ensuring reliability with structured output,
Speaker 1: giving users control with human in the loop features, and
Speaker 1: mastering that whole deployment, testing, monitoring, and continuous improvement cycle.
Speaker 2: It's a lot, It is a lot, but it's clear
Speaker 2: that llms, combined with powerful frameworks like line chain and
Speaker 2: lang graph are genuinely giving us well thing building superpowers.
Speaker 3: As the book puts it, they're.
Speaker 2: Making previously hard things easy and previously impossible things possible.
Speaker 2: This deep dive hopefully has equipped you, the listener, with
Speaker 2: the core knowledge to not just watch this revolution unfold,
Speaker 2: but to potentially participate.
Speaker 1: In it absolutely. And as we wrap up, maybe a
Speaker 1: final thought to chew on. As these llms become more capable,
Speaker 1: more edgentic, and more deeply integrated into our digital lives.
Speaker 1: Imagine that world where software isn't just a static toolbox anymore.
Speaker 1: Imagine it as a dynamic, intelligent collaborator, always learning, always adapting.
Speaker 1: How will that fundamental shift I packed our creativity, our productivity,
Speaker 1: even our very understanding of what it means to be
Speaker 1: well informed or in control in an age of potentially
Speaker 1: ambient AI constantly working around us.
Speaker 2: That's a fascinating future to contemplate.
Speaker 1: It really is. We definitely encourage you to continue your
Speaker 1: own deep dive. Explore the open source Langschaine library, check
Speaker 1: out lang graph, maybe look into langsmith. There's so much happening,
Speaker 1: and the joy of discovery in this field right now
Speaker 1: is truly immense.