Speaker 1: Welcome to the deep dive. If you've been watching the
Speaker 1: tech world, you know we're living through an incredible moment.
Speaker 1: This AI revolution powered by large language models or.
Speaker 2: LMS, it really is something else.
Speaker 1: Yeah, it's not just a minor tech update. It feels
Speaker 1: like one of those huge shifts, you know, like the computer,
Speaker 1: the internet, or the smartphone.
Speaker 3: Definitely pivotal.
Speaker 1: We're seeing these prototypes that seem almost magical. You can
Speaker 1: write stories, generate code, it's amazing.
Speaker 3: The demos are stunning.
Speaker 1: But here's the thing, right, taking that cool demo and
Speaker 1: making it a reliable, production grade application, that's well, that's
Speaker 1: a whole different.
Speaker 3: Credit, much harder game.
Speaker 1: Yeah. So our mission today is really to cut through
Speaker 1: some of that hype and navigate this pretty complex landscape
Speaker 1: of LM development. We want to equip you, our listener,
Speaker 1: with the core intuition, some surprising facts maybe, and the
Speaker 1: practical tools you'll need to build genuinely sophisticated applications, the
Speaker 1: ones that actually work.
Speaker 2: And to guide us on this deep dive. We're leaning
Speaker 2: pretty heavily on a fent plastic resource designing Large language
Speaker 2: Model applications by suhas PE. What's great about it? I
Speaker 2: think is that it's not just some dry technical manual.
Speaker 3: It gives this.
Speaker 2: Really holistic overview for you know, software engineers and mel folks,
Speaker 2: product managers, anyone involved.
Speaker 1: That's useful.
Speaker 2: Yeah, and it provides surprising depth that helps you understand
Speaker 2: not just what the models do, but fundamentally why they
Speaker 2: behave the way they do, and that why is crucial,
Speaker 2: absolutely crucial, especially for getting past fragile prototypes to something robust.
Speaker 1: Okay, let's unpack this then when we talk about lms,
Speaker 1: what are they actually made of? Like what are the
Speaker 1: basic ingredients before they even start learning?
Speaker 3: Right?
Speaker 2: So, at their very core, lms are built on pre
Speaker 2: training data.
Speaker 1: That's the raw fuel data. Got it?
Speaker 2: And you know that old saying garbage in, garbage out,
Speaker 2: It applies massively here. The scale and maybe even more importantly,
Speaker 2: the quality of this data is paramount.
Speaker 1: So where does it all come from?
Speaker 2: We're talking colossal amounts of text. A huge chunk often
Speaker 2: comes from web text, like from common Girl. Massive, but
Speaker 2: it needs so much cleaning because well, the Internet's.
Speaker 1: Messy understatement of the year, huh.
Speaker 3: Right.
Speaker 2: Then you have things like web text or open web text.
Speaker 2: They often use signals from places like Reddit outbound links
Speaker 2: specifically trying to filter for you know, higher quality stuff.
Speaker 3: Wisdom of the crowd kind of interesting.
Speaker 1: What else?
Speaker 2: There's factual knowledge from Wikipedia, super valuable for accuracy, but
Speaker 2: the style.
Speaker 1: Is very formal, yeah, very encyclopedic.
Speaker 2: And historically BooksCorpus was big, lots of narrative but surprisingly
Speaker 2: like twenty six percent romance novels from unpublished authors, so
Speaker 2: quite specific.
Speaker 1: Wow.
Speaker 2: Okay, and now you see newer efforts like hugging faces,
Speaker 2: Fine Web aiming for even cleaner web data and I'm
Speaker 2: fine Web ed you focusing on educational content.
Speaker 3: Fifteen trillion tokens. It's huge.
Speaker 1: So it's clearly not just about quantity, it's about cleaning
Speaker 1: and curating this raw material. What does that involve exactly?
Speaker 2: Data preprocessing is key, It's not glamorous, but maybe the
Speaker 2: most vital step, like what specifically you got to strip
Speaker 2: out all the web boilerplate, menus, navelinks, lar nipsen, placeholders,
Speaker 2: all that junk.
Speaker 3: And language identification.
Speaker 2: Is surprisingly tricky, even in supposedly English only data sets.
Speaker 3: Other languages creep in.
Speaker 2: If you don't catch that, your model might suddenly start
Speaker 2: speaking Spanish, which.
Speaker 1: Could be a bug or maybe a.
Speaker 2: Feature could be either, and quality filtering is vital too
Speaker 2: often using things like perplexity scores.
Speaker 1: Okay, perplexity scores, how does that work? Break that down?
Speaker 3: Sure? Think of it like this.
Speaker 2: If you're trying to predict the next word in a
Speaker 2: really well written, clear sentence, it's pretty easy.
Speaker 3: Low uncertainty. That's low perplexity, makes sense. But if you're
Speaker 3: trying to.
Speaker 2: Guess the next word in some garbled text full of errors,
Speaker 2: it's super hard. High uncertainty, that's high perplexity.
Speaker 1: Ah, okay, So high perplexity means noisy, bad data.
Speaker 2: Basically, yeah, you probably don't want to feed that to
Speaker 2: your expensive model.
Speaker 1: Got it? And after cleaning you mentioned duplication in privacy?
Speaker 1: Why is that so important?
Speaker 3: Oh? It's massive.
Speaker 2: Web tex is full of duplicates. Removing them isn't just
Speaker 2: about efficiency. It's critical to stop llms from accidentally memorizing
Speaker 2: and leaking PII personally identifiable information.
Speaker 1: Right, even if it's technically published exactly.
Speaker 2: That's the whole contextual integrity issue. Should an AI just
Speaker 2: blurt out someone's address because it found it online somewhere?
Speaker 2: It's tricky, especially with public figures.
Speaker 1: Complex ethical grounds totally.
Speaker 2: And what's wild is that even a tiny bit of
Speaker 2: manipulated data, like less than point one percent, can potentially
Speaker 2: make it easier for other sensitive data to leak.
Speaker 1: Wow. Okay, that's a lot about the raw material. But
Speaker 1: this next part for me is where it gets really fascinating.
Speaker 1: How do these models actually read. It's not like they
Speaker 1: see words like we do, is it?
Speaker 3: You're spot on?
Speaker 2: They don't process discrete words like humans. They use something
Speaker 2: called tokens, and often these tokens are.
Speaker 1: Subwords, subwords like parts of words kind of yeah.
Speaker 2: So example in gpt ex, office might be one token,
Speaker 2: but office with that little meaning a space before it
Speaker 2: is a different token.
Speaker 3: Case matters to office versus office.
Speaker 1: Okay, so it's more granial exactly.
Speaker 2: And the subword approach is clever because it mostly avoids.
Speaker 3: The out of vocabulary problem.
Speaker 2: If it sees a totally new word, it can usually
Speaker 2: break it down into known subword pieces instead of just crashing.
Speaker 1: So it's almost like they're reading in syllables or morphemes,
Speaker 1: not whole words.
Speaker 2: Oh a bit like that, Yeah, smaller meaningful units. And
Speaker 2: sometimes this process creates weird artifacts glitch tokens tokens, yeah,
Speaker 2: or undertrain ones. There's this great story about solid Magic
Speaker 2: gold Carp. It was a Reddit username that actually became
Speaker 2: a token in GPT two.
Speaker 1: Seriously a username yep.
Speaker 2: But then later models like GPT three were trained on
Speaker 2: different data where that token barely appeared. It had no
Speaker 2: training signal. So if you fed GPT three Solid Magic
Speaker 2: old Carp, it would just act weirdly like it had
Speaker 2: no clue what to do as it are it is.
Speaker 2: But it raises this cool question, what can these weird
Speaker 2: tokens tell us about the training data. It's like a
Speaker 2: little window into the model's digestive system.
Speaker 1: Huh, a digital digestive system. I like that. Okay, so
Speaker 1: we have the data, we have the tokens. How does
Speaker 1: this all come together? We hear neural networks transformers. What's
Speaker 1: the engine?
Speaker 3: Right?
Speaker 2: The engine at the heart of almost all modern llms
Speaker 2: is the transformer architecture. It was a huge breakthrough back
Speaker 2: in twenty seventeen. Why was it such a big deal
Speaker 2: because older recurrent neural networks RNNs really struggled with long sentences,
Speaker 2: long range dependencies. They were trying to sort of cram
Speaker 2: the meaning of a whole sentence into one single vector.
Speaker 2: It just didn't scale well. The transformer changed that with
Speaker 2: its key innovation self attention.
Speaker 1: Self attention. That sounds mindful. How does it work for AI?
Speaker 2: Heah, Yeah, it's actually pretty intuitive. Think about how we read.
Speaker 2: We don't give every word equal weight, right, definitely not.
Speaker 2: We focus on certain words to understand context, like bank
Speaker 2: means something different in riverbank versus saving bank. Self attention
Speaker 2: lets the model do exactly that way, the importance of
Speaker 2: different words in the sequence as it processes them, so it.
Speaker 1: Learns context from surrounding words precisely.
Speaker 2: It's like that old linguistics idea you shall know a
Speaker 2: word by the company it keeps. It uses these things
Speaker 2: called query key and value matrices to let words mathematically
Speaker 2: attend to each other.
Speaker 1: Okay, mathematically attend, got it? And they're different types of
Speaker 1: these transformers.
Speaker 3: Yeah, broadly. Three main transformer backbones.
Speaker 2: First, encoder only models like Burt great for understanding text things,
Speaker 2: search or classification.
Speaker 1: Right.
Speaker 2: Then the original encoder decoder design still fantastic for things
Speaker 2: like machine translation, where you need to process an input
Speaker 2: and generate a distinct output.
Speaker 1: Okay.
Speaker 2: And finally, the one we usually associate with generative AI
Speaker 2: like GPT four the decoder only architecture. These models are
Speaker 2: specialized in predicting the very next token in a sequence.
Speaker 2: That's how they generate text.
Speaker 1: And what about these mixture of experts models? Loe? Are
Speaker 1: they different? Again?
Speaker 2: There are really interesting evolution sort of built on the backbone.
Speaker 2: Mixture of experts aims to massively increase a model's capacity
Speaker 2: how much it knows without proportionally increasing the compute cost
Speaker 2: for every single input.
Speaker 1: How does that work?
Speaker 2: The clever bit is that for any given input, only
Speaker 2: a subset of specialized experts inside the model gets activated.
Speaker 2: So ask about physics, the physics expert activates. Ask about poetry,
Speaker 2: The poetry expert lights up. The others stay quiet.
Speaker 1: Huh So it's like calling on specialists exactly.
Speaker 2: You get the power of a huge model, but you
Speaker 2: only run the relevant parts for each query. Mistral's mixtral
Speaker 2: is a key example, and many suspect GEPC four uses
Speaker 2: something similar, though it's unconfirmed.
Speaker 1: This is really critical then for you, the listener, understanding
Speaker 1: these foundations, the data, the tokens, the transformer architecture MOE.
Speaker 1: It's crucial, absolutely, even if you never train one from scratch.
Speaker 1: That intuition helps you debug, figure out why it's behaving oddly,
Speaker 1: and build better apps. You start to see why it
Speaker 1: might struggle or succeed.
Speaker 3: Get a feel for the machine.
Speaker 1: So we've established these models are powerful, but yeah, definitely
Speaker 1: not perfect. What are some of the biggest practical limitations
Speaker 1: and how are we starting to tackle them.
Speaker 2: One of the biggest and probably most talked about, is hallucinations.
Speaker 1: Right when they just make stuff up.
Speaker 2: Exactly more formally, it's generated text that isn't grounded in
Speaker 2: the training data or the input context.
Speaker 3: It sounds plausible, but it's just fabrications.
Speaker 1: Do you give an example.
Speaker 2: Sure, there was a well known case with the NAS
Speaker 2: Research Hermes model. It hallucinated details about Ugandan medal winners
Speaker 2: from the twenty twenty Olympics. Oh wow, Yeah, it got
Speaker 2: birth dates wrong, mixed up which medals they won. The
Speaker 2: athletes were real, the core facts were real, but the
Speaker 2: details were just invented, confidently stated but wrong.
Speaker 1: Yikes, how do you even begin to fix that?
Speaker 2: It's tough. Mitigation involves several things. Good product design helps
Speaker 2: try not to ask questions the LLM likely can't answer,
Speaker 2: soh knowing what it doesn't know is hard true. We
Speaker 2: also look at model self knowledge and calibration, basically, how
Speaker 2: confident is the model in its own output. Sometimes low
Speaker 2: confidence correlates with higher hallucination risk.
Speaker 1: Okay, using its own uncertainty signals.
Speaker 3: Yeah.
Speaker 2: And then there are technical effixes during generation, like factual
Speaker 2: nuclear sampling, which tries to reduce randomness for more factual outputs,
Speaker 2: or doulity coding, which cleverly uses differences between signals and
Speaker 2: the transformulators to spot potential hallucinations.
Speaker 1: Fascinating, And sometimes they hallucinate just because the prompt itself
Speaker 1: is confusing, right, like yeah, with irrelevant info?
Speaker 2: Yeah, absolutely. If you put distracting sentences in the prompt
Speaker 2: like mentioning max selling apples in Sarah's unrelated math problem,
Speaker 2: the LM can get confused and incorporate the wrong info,
Speaker 2: so prompting it to first identify and remove irrelevant context
Speaker 2: can help.
Speaker 1: Okay. So beyond just factual accuracy, what about actual reasoning?
Speaker 1: Can they really connect dots logically?
Speaker 2: That's a huge area of research and development. Natural language
Speaker 2: reasoning means integrating knowledge to draw conclusions, and there are
Speaker 2: different kinds. Deductive is pure logic premise a premise B,
Speaker 2: therefore conclusion C. Like mister Shockley is allergic to mushrooms.
Speaker 2: This dish has mushrooms, so mister Shockley.
Speaker 3: Should avoid it.
Speaker 2: Simple logic, then inductive generalizing from examples, so hundreds of
Speaker 2: round manhole covers conclude manhole covers are generally round. Abductive
Speaker 2: reasoning is finding the most likely explanation streets, wet puddles, umbrellas, hmm,
Speaker 2: probably rain inferance of the best explanation exactly. And then
Speaker 2: there's common sense implicit stuff like you can't fit a
Speaker 2: horse in a Mini Cooper.
Speaker 1: Huh yeah, hopefully obvious. How do you get an LLM
Speaker 1: to do that better?
Speaker 2: A major technique is chain of thought prompting or cooey.
Speaker 2: You literally tell the LLM to think, step by.
Speaker 1: Step, show your work basically pretty.
Speaker 2: Much for a math problem like thirty four plus forty
Speaker 2: four plus three twenty three three to two. Instead of
Speaker 2: just asking for the answer, you ask it to break
Speaker 2: it down first, calculate three, two, three, and so on.
Speaker 2: Performance jump dramatically.
Speaker 1: Because it forces a sequential process right.
Speaker 2: It gives it intermediate steps to work with. It costs
Speaker 2: more tokens, more time, but it's often worth it for
Speaker 2: complex tasks. You can also use verifiers, maybe another LM
Speaker 2: to check the steps, or even fine tune models specifically
Speaker 2: on reasoning data sets.
Speaker 1: Okay, so we can make them smarter, more reliable, but
Speaker 1: these things are huge. How do we actually run them
Speaker 1: efficiently in the real world. That sounds like a massive hurdle.
Speaker 2: It is a huge hurdle, and that brings us to
Speaker 2: choosing and optimizing llms for production. First, you have to
Speaker 2: pick one. You've got proprietary providers open AI, Google, Mthropic
Speaker 2: via APIs easy to use, manage the big players, and
Speaker 2: then open source models Metaslama, luther AI, Mistral, Microsoft's FI models.
Speaker 2: You get the model weights, more transparency, more flexibility, but
Speaker 2: you often have to manage the deployment yourself or use
Speaker 2: specialized platforms.
Speaker 1: Trade offs there big time.
Speaker 2: Transparency versus convenience, cost versus latency, And.
Speaker 1: Once you pick one, how do you know if it's
Speaker 1: any good for your job? Benchmarks are everywhere, but are
Speaker 1: they the whole story?
Speaker 2: Definitely not the whole story. Evaluating llm's is super tricka.
Speaker 2: Benchmarks can suffer from test set contamination. The model might
Speaker 2: have seen the answers in its training data cheating basically
Speaker 2: kind of or models get over optimized just to score
Speaker 2: well on a benchmark, but aren't great in practice, and
Speaker 2: they're very sensitive to how you prompt them. Frameworks like
Speaker 2: Stanford's HLM try to be more comprehensive, looking at accuracy, robustness, fairness, calibration.
Speaker 3: Lots of things.
Speaker 2: So you need holistic evaluation and ideally your own internal
Speaker 2: benchmarks tailored to your actual use case. That's key now
Speaker 2: actually running them, you generally need GPUs for decent speed.
Speaker 2: They're just computationally intensive, right, expensive hardware, which is where
Speaker 2: quantization comes in.
Speaker 3: It's a lifesaver.
Speaker 1: Quantization Explain that sounds.
Speaker 3: Complex, it's actually a pretty neat idea.
Speaker 2: It's about reducing the memory footprint. You take the numbers
Speaker 2: inside the model, usually high precision floating point numbers like
Speaker 2: FP thirty two, and represent them with fewer bits like
Speaker 2: FP sixteen, b F sixteen or even eight bit integers.
Speaker 2: It's an INN eight so like compressing the numbers.
Speaker 1: Exactly like compressing them, you lose a tiny bit of precision,
Speaker 1: usually negligible, but the model becomes much smaller, uses less memory,
Speaker 1: and runs faster. Tools like a LAMA. Make it easier
Speaker 1: to run these quantized models, even locally on a powerful laptop.
Speaker 2: Sometimes that's cool. Make the more accessible. Okay, so it's loaded,
Speaker 2: maybe quantized. How do you speed up the inference, the
Speaker 2: actual running part, and make it cheaper.
Speaker 1: Several key tricks for LLM inference optimization. A huge one
Speaker 1: is the cav cash cav cash Yeah, key value cash.
Speaker 1: Think of it as the model's short term memory for
Speaker 1: the current conversation or task. When you send a prompt,
Speaker 1: especially one with instructions, those instructions often stay the same
Speaker 1: for follow up questions.
Speaker 2: The cav cash stores the internal calculations the key and
Speaker 2: value matrices from self attention related to that initial prompt,
Speaker 2: so the model doesn't have to recalculate them every single
Speaker 2: time you ask a follow up question. It dramatically speeds
Speaker 2: things up after.
Speaker 3: The first turn.
Speaker 1: Ah avoids redundant work clever what.
Speaker 2: Else, there's speculative decoding. This is pretty cool. You use
Speaker 2: a small, fast draft model to generate a chunk of
Speaker 2: tokens quickly, Then the larger, more accurate model verifies those
Speaker 2: tokens in a batch, like.
Speaker 1: A quick first draft, and then a careful.
Speaker 2: Edit exactly the big model checks the interns work quickly
Speaker 2: instead of doing it all slowly. Itself speeds things up
Speaker 2: a lot for generation nice. We also use knowledge distillation.
Speaker 2: Train a smaller student model to mimic a big teacher model.
Speaker 2: You get a faster, cheaper model that retains a lot
Speaker 2: of the capability.
Speaker 1: Think the stillburd right, smaller but still capable.
Speaker 2: And things like parallel de coding for generating multiple parts simultaneously,
Speaker 2: or early exit where simpler queries might get an answer
Speaker 2: from an earlier layer of the model without going all
Speaker 2: the way through lots of techniques to.
Speaker 1: Make them practical. Okay, this brings us squarely to the
Speaker 1: application layer. How do we take these optimized lmms and
Speaker 1: actually plug them into complex software? They can't just operate
Speaker 1: in a vacuum, can they?
Speaker 2: No, definitely not. They have real limitations. Knowledge cutoff is
Speaker 2: a big one. They don't know about yesterday's news unless retrained.
Speaker 2: They struggle with precise math, no factual guarantees, can't easily
Speaker 2: cite sources and context. Windows while growing are still finite.
Speaker 1: So they need help from the outside world.
Speaker 3: Precisely.
Speaker 2: You need to interface them with external tools and data.
Speaker 2: We generally talk about three core LLM interaction paradigms. Okay, First,
Speaker 2: the passive approach. This is basically retrieval augmented generation or RG.
Speaker 2: The LLM just receives information and its prompt. It doesn't
Speaker 2: know where it came from. You feed it the relevant context.
Speaker 1: Giving it the answer key snippet.
Speaker 2: Kind of yeah, perfect for Q and A over your
Speaker 2: own private documents. You retrieve the relevant text, put it
Speaker 2: in the prompt, and the LLM answer.
Speaker 3: Is based on that.
Speaker 1: Okay, passive, what's next?
Speaker 3: Explicit tool use here? The LLM is more active.
Speaker 2: You give it instructions and a set of tools it
Speaker 2: can use, like a web search tool, a calculator, a
Speaker 2: database connector, and.
Speaker 1: It chooses which tool to use exactly.
Speaker 2: Frameworks like lang chain help manage this. The LLM decides, okay,
Speaker 2: to answer this, I need to search the web, and
Speaker 2: it triggers the search tool. It becomes an orchestrator, more interactive.
Speaker 1: And the third, the.
Speaker 2: Most advanced, is the aegentic paradigm. Think autonomous agents. These
Speaker 2: lllms can interact with their environment, break down complex goals
Speaker 2: into subtasks, and take a sequence of actions using tools
Speaker 2: to achieve the goal.
Speaker 1: Like that Apple CFO example you.
Speaker 2: Mentioned earlier, Exactly like that, who was Apple CFO at
Speaker 2: its lowest stock price in ten years? The agent figures
Speaker 2: out one, get stock data, two, find lowest point three
Speaker 2: find CFO for that date. It plans and executes.
Speaker 1: Wow, that's powerful. Still limitations though, you said.
Speaker 2: Oh yeah, current agents can still get stuck in loops,
Speaker 2: choose the wrong tool, or just fail. It's definitely the
Speaker 2: frontier very active research.
Speaker 1: Okay, but let's go back to Eric retrieval augmented generation.
Speaker 1: You said it's passive, but it feels like the cornerstone
Speaker 1: of so many practical LLM apps today. Let's really dive
Speaker 1: deep into OURG. Why is it so vital and how
Speaker 1: does it actually work?
Speaker 3: Under the hood, OURAG is absolutely fundamental.
Speaker 2: Its main job is letting LMS access your specific private
Speaker 2: data stuff it never saw.
Speaker 1: During training, right bitging the knowledge gap.
Speaker 2: Exactly, and by doing that it drastically reduces hallucinations because
Speaker 2: responses are grounded in actual provided text. It allows for citations,
Speaker 2: It lets the LM talk about recent events, and it
Speaker 2: handles the long tail entities.
Speaker 1: Long tail entities what are those again? Think?
Speaker 3: Really niche facts stuff? So rare.
Speaker 2: It might only appear once or twice in trillions of
Speaker 2: tokens of training data. LM struggle to memorize that OURG
Speaker 2: retrieves that specific fact just when needed. Without our AG,
Speaker 2: you'd need impossibly huge models to maybe memorize everything.
Speaker 1: So OURG is essential for accessing specific, less common knowledge totally.
Speaker 2: The RAG pipeline itself is actually quite sophisticated.
Speaker 1: Now.
Speaker 3: It's not just a simple.
Speaker 1: Look up, okay, walk us through the steps.
Speaker 2: It often starts with rewrite. The user's query might get
Speaker 2: rephrase to better match the documents. Sometimes an LM even
Speaker 2: generates a hypothetical document high D that would answer the query,
Speaker 2: and then you search using.
Speaker 1: That clever search for the ideal answer.
Speaker 2: Shape kind of then retrieve, fetching potentially relevant documents, often
Speaker 2: using embeddings for semantic similarity. But other methods exist too,
Speaker 2: like generative retrieval, where the LLM predicts document IDs.
Speaker 1: Okay, got a pile of potential documents, then.
Speaker 2: Rerank That initial retrieval might grab some irrelevant stuff. Reranking
Speaker 2: uses another model, often a smaller specialized one, to score
Speaker 2: and reorder the retrieved docs by true relevance quality.
Speaker 1: Control, refining the results. What's next?
Speaker 2: Refine Now, you might shorten or summarize the relevant snippets
Speaker 2: to fit the context window better and make them more useful.
Speaker 2: Techniques like chain of note use an LLM to generate
Speaker 2: summaries or bullet points, highlighting key info from the retrieved.
Speaker 1: Chunks, making it digestible for the main LLM precisely.
Speaker 2: Then insert This is just about how you put that
Speaker 2: refined context into the final prompt. Turns out lllms often
Speaker 2: pay more attention to stuff at the very beginning or
Speaker 2: very end of the prompt attention biases.
Speaker 1: Huh interesting quirk.
Speaker 3: Yeah.
Speaker 2: Finally, generate the main LLM produces the answer, grounded by
Speaker 2: the context you just carefully prepared and inserted. That's the
Speaker 2: basic flow well, and it can get even more complex
Speaker 2: techniques like flare, interleave generation and retrieval. The LM starts writing,
Speaker 2: identifies a bit it's unsure about low confidence tokens, pauses,
Speaker 2: retrieves more specific info just for that gap, than continues generating.
Speaker 1: Wow, dynamically retrieving info mid sentence. That's intricate. Now, with
Speaker 1: context windows getting huge like Gemini one point five pros
Speaker 1: million tokens, does rags start to become less important. Can't
Speaker 1: we just stuff everything in the context.
Speaker 2: That's a really common question, and maybe counterintuitively, no, our
Speaker 2: rag is still crucial, maybe even more crucial. It is
Speaker 2: that because even a million tokens of real world data
Speaker 2: can be incredibly noisy, finding that one specific fact the
Speaker 2: needle in the haystack, is still hard for the LLM.
Speaker 2: RI provides that targeted grounding. It ensures factuality and allows
Speaker 2: citations in a way that just having a massive context
Speaker 2: window doesn't guarantee.
Speaker 1: So long context helps fit more potential info, but ARI
Speaker 1: helps find the right info within it exactly.
Speaker 2: It reduces the noise problem. Long context isn't a magic
Speaker 2: bullet for messy data or the need for verifiable grounding,
Speaker 2: and often are and fine tuning work together. You might
Speaker 2: fine tune the retriever, the reranker, the generator, each part benefits.
Speaker 1: Okay, that makes sense. So given all this complexity, RI pipelines, optimization,
Speaker 1: choosing models, how do developers actually build and manage these systems?
Speaker 1: It sounds way beyond just fiddling with prompts.
Speaker 2: Oh, it absolutely is. It requires real system design. One
Speaker 2: common pattern is MULTILLLM.
Speaker 1: Architectures using more than one LM.
Speaker 2: Yeah, maybe a small, fast, cheap model for simple tasks
Speaker 2: or routing the query, then escalating to a big, powerful
Speaker 2: model for the complex reasoning A cascade or a router
Speaker 2: setup balances cost and capability. And the programming paradigms are
Speaker 2: evolving too. We're moving away from just endless manual prompt engineering.
Speaker 1: Thank goodness.
Speaker 2: Right. Frameworks like DSPI are pushing this idea of programming
Speaker 2: not prompting.
Speaker 1: DSPI tell me more.
Speaker 2: It abstracts the prompting process. You define the flow using
Speaker 2: modules that represent techniques like chain of Thought or React.
Speaker 2: Then DSPI figures out the best prompt and even model
Speaker 2: parameters to make that flow work for your data, often
Speaker 2: through optimization. It separates the program logic from the prompt tinkering.
Speaker 1: So the framework optimizes the prompts for you largely.
Speaker 2: Yes, it angks for more robust, generalizable applications.
Speaker 1: That's a big shift. What about controlling the output format
Speaker 1: more strictly?
Speaker 2: For that, look at things like LMQL. It stands for
Speaker 2: Language Model Query Language. It's like SQL but for.
Speaker 3: Llms embedded in Python.
Speaker 1: Okay.
Speaker 2: It lets you write prompts but add declarative constraints like
Speaker 2: you could define a template for generating a jet pre clue,
Speaker 2: then add a wear clause in LMQL saying the output
Speaker 2: must be phrased as a question and be exactly three
Speaker 2: words long.
Speaker 1: Ah, so you enforce structure directly.
Speaker 2: Exactly, much more control over the output format than just
Speaker 2: asking nicely in the prompt. Really useful for structured data
Speaker 2: extraction or making the LM fit into existing systems.
Speaker 1: What an incredible journey we've been on really, from the
Speaker 1: fundamental building blocks that messy data, the weirdness have tokens
Speaker 1: the transformer all the way to these advanced techniques are
Speaker 1: gree optimization, ds PI, LMQL, making them robust, efficient, integrated.
Speaker 3: Yeah, it's a lot to cover.
Speaker 1: It really shows these lemms aren't just inscrutable black boxes,
Speaker 1: are they They're complex, yes, but they can be steered, optimized,
Speaker 1: connected to the world.
Speaker 2: Absolutely, they are tools we can shape and direct. And
Speaker 2: maybe that's the provocative thought to leave you with. This
Speaker 2: field is moving so incredibly fast. What we discussed today
Speaker 2: as best practice, it might honestly be different in six
Speaker 2: months or a year.
Speaker 1: Constant change, constant change.
Speaker 2: So the real lasting value for you as someone Learning
Speaker 2: or building in this space isn't just memorizing today's specific techniques.
Speaker 2: It's getting that deeper, intuitive grasp of the underlying.
Speaker 1: Principles, Understanding the why.
Speaker 2: Understanding the why, how they work, why they fail, how.
Speaker 3: To nudge them.
Speaker 2: That intuition will be your best asset as the technology
Speaker 2: keeps evolving at this frankly breathtaking pace.
Speaker 1: To keep exploring, keep asking questions, and keep building.
Speaker 3: That's where the real learning happens.