Speaker 1: Welcome to the deep dive. We take a whole stack
Speaker 1: of information articles, research our notes, and really try to
Speaker 1: pull out the key insights for you.
Speaker 2: Right the goal is always to cut through that complexity, get.
Speaker 1: To the useful stuff exactly, and help you unlock the
Speaker 1: power of these well cutting edge tools. Today we're diving
Speaker 1: deep into prompt engineering for generative AI.
Speaker 2: It's a huge topic right now, it really.
Speaker 1: Is, and we're working from a fantastic resource today, the
Speaker 1: book Prompt Engineering for Generative AI by James Phoenix and
Speaker 1: Mike Taylor. It's been called a lighthouse in this sort
Speaker 1: of vast ocean of AI.
Speaker 2: That's a good way to put it. Yeah, this deep
Speaker 2: dive is really giving you a shortcut, a way to
Speaker 2: understand how to get reliable, high quality results from AI models.
Speaker 1: Whether it's text or images right exactly.
Speaker 2: Text or images. It's about, as books says, kind of
Speaker 2: future proofing your inputs for reliable AI outputs at scale.
Speaker 1: And that's so important because wow, the pace of change
Speaker 1: with generative AI is just it's breakneck speed. It's actually
Speaker 1: hard to keep up.
Speaker 2: Sometimes it really is every week something new.
Speaker 1: So let's start at the beginning. What is prompt engineering,
Speaker 1: Why does it matter so much?
Speaker 2: Okay? So, at its core, prompt engineering is basically the
Speaker 2: art and well the science of crafting the right funt inputs,
Speaker 2: the prompts you give the AI to get the outputs
Speaker 2: you actually want, the instructions essentially exactly clear instructions. And
Speaker 2: it matters a lot because what you put in fundamentally
Speaker 2: changes the probability of every single word or pixel the
Speaker 2: AI generates.
Speaker 1: Next ah probability.
Speaker 2: Yeah. Plus, you know models like open AIS they charge
Speaker 2: based on tokens. Used tokens are kind of like pieces
Speaker 2: of words.
Speaker 1: So the length and quality of your prompt directly impacts
Speaker 1: costs directly.
Speaker 2: So optimizing prompts isn't just about quality, it's crucial for
Speaker 2: cost and reliability to getting it right saves money and headaches.
Speaker 1: Okay, that makes total sense, like briefing someone properly before
Speaker 1: they start a task. So let's dive in. What are
Speaker 1: those foundational principles, the ones that work no matter which
Speaker 1: AI model you're using.
Speaker 2: Right, Let's focus on three core principles these really hold
Speaker 2: up over time. First, one, and maybe the most common
Speaker 2: pitfall people run into, is you need to give direction,
Speaker 2: be specific, be specific, brief the AI on exactly what
Speaker 2: you wanted to do. So instead of just saying brainstorm
Speaker 2: product names for a shoe, which is.
Speaker 1: Okay, a bit vague, right.
Speaker 2: Vague, you'd get much better results. Adding context like brainstorm
Speaker 2: product names for a shoe that fits any foot size
Speaker 2: in the style of Steve Jobs, or you know like
Speaker 2: Elon Musk wouldn't.
Speaker 1: Eame it ah, Okay. Adding that constraint really narrows it
Speaker 1: down for the AI precisely.
Speaker 2: And the sources we looked at they really emphasize that
Speaker 2: too little direction is the number one problem. That's why
Speaker 2: AI sometimes seems to well misunderstand you.
Speaker 1: Okay, So clear direction first. Once you've got that, what's
Speaker 1: the next key thing for getting predictable results.
Speaker 2: Next up is specify format. This is huge. AI models
Speaker 2: are incredible universal translators, not just between say French and English,
Speaker 2: but between data structures. I think JSON to YAMEL or
Speaker 2: even just natural language to Python code.
Speaker 1: Wow.
Speaker 2: So it's really important to tell the AI what format
Speaker 2: you want the answer in. If you don't, especially if
Speaker 2: you're building software that relies on.
Speaker 1: This, Yeah, I can see that you.
Speaker 2: Might sometimes get a numbered list when you expected comma
Speaker 2: separated values or something like that, and that could just
Speaker 2: break your whole process.
Speaker 1: So specifying format prevents those kinds of errors. Can you
Speaker 1: ask for complex stuff?
Speaker 2: Absolutely? You can ask for really complex formats like Mermaid
Speaker 2: syntax for generating flow diagrams. It's surprisingly capable.
Speaker 1: That's powerful, especially for developers. Okay, so direction format. What's
Speaker 1: the third pillar?
Speaker 2: The third one is provide examples Sometimes, honestly, it's just
Speaker 2: easier to show the AI what you like instead of
Speaker 2: trying to describe it perfectly.
Speaker 1: Like show, don't just tell exactly.
Speaker 2: This works really well if you're maybe not an expert
Speaker 2: in the specific domain yourself. Let's say you want product
Speaker 2: names but in a very particular kind of quirky style. Okay,
Speaker 2: instead of trying to describe quirki, you just give examples
Speaker 2: like eyebar, fridge, iverdge beer. I time, the AI sees
Speaker 2: that pattern immediately, Ah.
Speaker 1: I see, it learns the style from the examples. How
Speaker 1: many examples work best?
Speaker 2: Usually just adding one to three examples almost always helps.
Speaker 2: It gives the AI a much clearer target. You just
Speaker 2: need to be mindful of the token limits.
Speaker 1: Right, the character limits for the prompt.
Speaker 2: Yeah, like mid journey. The image generator takes about six
Speaker 2: thousand characters free chat GPT is more like thirty two thousand,
Speaker 2: so you usually have space for a few good examples
Speaker 2: without any trouble.
Speaker 1: Okay, so give direction, specify format, provide examples. Those are
Speaker 1: the fundamentals. But let's level up for people wanting to
Speaker 1: use AI professionally. How do you get it to do
Speaker 1: more complex things like generating structured data, transforming text, maybe
Speaker 1: even checking its own work.
Speaker 2: Absolutely, this is where it moves from you know, just
Speaker 2: experimenting to really building things. Let's start with generating structured outputs.
Speaker 2: This goes way beyond simple lists. Okay, you can get
Speaker 2: the A to generate really complex mested data structures. The
Speaker 2: book mentions things like hierarchical lists JSON Yamal, like.
Speaker 1: Creating a database ready structure exactly.
Speaker 2: Imagine generating a detailed article outline perfectly formatted as a
Speaker 2: Jason payload, or taking a user's casual request and turning
Speaker 2: it into a structured Yamal shopping list. The precision is incredible.
Speaker 1: Wow, does it always get it right like perfectly valid
Speaker 1: Jason every time?
Speaker 2: Not always. Language models can sometimes add extra conversational text
Speaker 2: or maybe generate slightly invalid Jason or Yamal, But there
Speaker 2: are smart ways strategies to handle those kinds of edge
Speaker 2: cases in your code.
Speaker 1: Okay, good to know and beyond JASAML.
Speaker 2: Yeah, it can even generate things like mock CSV data,
Speaker 2: you know, a list of fake names, ages, grades, whatever
Speaker 2: you need ready to use in spreadsheets or other tools.
Speaker 2: It's like having an instant data engineer.
Speaker 1: That is genuinely powerful for automation. Okay, so that's generating
Speaker 1: structured stuff, but what about working with texts that already exists, transforming,
Speaker 1: simplifying it, analyzing it right.
Speaker 2: Huge area. There's several really cool techniques here. One that's
Speaker 2: super popular and useful is explain it like I'm five
Speaker 2: ELI five, yet exactly ELI five. It's not just a gimmick.
Speaker 2: It's a seriously powerful way to take dense technical documents
Speaker 2: think medical abstracts or complex legal text and boil them
Speaker 2: down into language anyone can grasp. It really helps democratize information.
Speaker 1: That's fantastic.
Speaker 2: What else, then, there's universal translation. We mentioned language to language,
Speaker 2: but lllms can also translate between coding languages like Python
Speaker 2: to JavaScript or vice versa. They act as this amazing
Speaker 2: bridge TREA.
Speaker 1: Do communication gaps? Okay, but what if the AI doesn't
Speaker 1: have enough information to give a good answer, can it
Speaker 1: like ask for more detail?
Speaker 2: Yes? Absolutely, you can teach it to ask for context.
Speaker 2: Llm's can function as sort of simple agents with some
Speaker 2: reasoning ability. You can actually prompt them to recognize when
Speaker 2: they lack info and then ask you clarifying questions.
Speaker 1: Oh interesting, So it becomes more of a.
Speaker 2: Dialogue exactly like if you ask should I use Mango
Speaker 2: dB or POSTGRESCOO, a well prompted GBT four might come
Speaker 2: back with okay to answer that, I need to know
Speaker 2: what's your data structure? Like what are your scalability needs?
Speaker 2: Do you need acid compliance? And so on?
Speaker 1: So it guides you to give it the info at
Speaker 1: needs for a better answer.
Speaker 2: It's smart, very smart, turns it into an active problem solver.
Speaker 2: Another really neat one is text style unbundling unbundling.
Speaker 1: What's that?
Speaker 2: It means you can get the AI to analyze a
Speaker 2: piece of text and extract its specific stylistic features the tone, sentence, length,
Speaker 2: vocabulary choices, even the structure.
Speaker 1: Okay, and then what then you can.
Speaker 2: Use those extracted features? Is a kind of style guide
Speaker 2: to generate new content that matches that original voice perfectly.
Speaker 2: Super useful for businesses wanting consistent brand messages.
Speaker 1: Ah I see maintaining a consistent voice across different pieces
Speaker 1: of content crucial for branding totally. Now, what about just
Speaker 1: dealing with huge amounts of text, like reading massive reports
Speaker 1: or research papers.
Speaker 2: That's where summarization and chunking come in. AI. Summarization is
Speaker 2: amazing for distilling information, but for really long documents you
Speaker 2: hit those context limits we talked about, right.
Speaker 1: The AI can only remember so much text at once.
Speaker 2: Exactly, so chunking, just breaking the text into smaller, manageable pieces,
Speaker 2: is essential. It lets you process long documents, even ones
Speaker 2: covering multiple topics, without overwhelming the AI.
Speaker 1: How do you decide where to split the texts?
Speaker 2: There are different ways. You can split by sentence, paragraph,
Speaker 2: sometimes by complexity, or just by length, or you can
Speaker 2: get really precise and split by the actual token count
Speaker 2: using specific tools, especially for models like open ais ensures
Speaker 2: each chunk fits perfectly.
Speaker 1: Okay, smart ways to handle large inputs. But now we've
Speaker 1: generated all this output, how do we know if our
Speaker 1: prompts are actually any good? How do we evaluate the
Speaker 1: quality rigorously great question.
Speaker 2: Evaluating prompt quality is key if you're serious. You can
Speaker 2: start simple, like with the thumbs up thumbs down rating
Speaker 2: system at the bit of Rigger.
Speaker 1: Okay, basic feedback.
Speaker 2: But you can get much more sophisticated. Automated evaluation is
Speaker 2: totally possible. For instance, you could use a powerful model
Speaker 2: like GPT four to actually grade the responses from a
Speaker 2: less powerful model AI.
Speaker 1: Evaluating AI interesting using the best model to check the others.
Speaker 2: Yeah, and the book talks about proper ab testing methods.
Speaker 2: Often using tools like Jupiter notebooks, you can do things
Speaker 2: like shuffler responses, so the human rader is blind to
Speaker 2: which prompt variation produced which output, avoiding bias.
Speaker 1: Proper scientific method basically exactly.
Speaker 2: You can even compare prompt variations using metrics like embedding
Speaker 2: distance that measures how semantically similar an AI's answer is
Speaker 2: to a known ground truth or perfect.
Speaker 1: Answer, so measuring how close it is and meaning right.
Speaker 2: The whole point is to iterate faster, more scientifically and
Speaker 2: reduce the need for tons of slow, expensive manual review.
Speaker 1: It's incredible how fast this field is moving, not just
Speaker 1: the prompting techniques but the underlying AI model themselves, and
Speaker 1: the frameworks built on top of them feels like warp
Speaker 1: speeds sometimes.
Speaker 2: Oh, absolutely, the pace of innovation is just staggering. If
Speaker 2: we take a brief history of text generation models, the
Speaker 2: big leap was the transformer architecture back around twenty seventeen.
Speaker 1: Right, that changed everything.
Speaker 2: It really did allowed models to connect words across long
Speaker 2: distances in text, boosting comprehension and efficiency. Then you had
Speaker 2: open ais GPT series, GPT two, GPT three, three point
Speaker 2: five Turbo, Chat GPT now GPT four really pushing things
Speaker 2: into the public eye.
Speaker 1: GPT three point five Turbo and chat GPT made it accessible.
Speaker 2: Yeah, three point five Turbo, especially with Microsoft's investment, brought
Speaker 2: better efficiency and lower costs, made lllms practical for more people.
Speaker 2: And Chat GPT fine Tune for conversation just exploded fastest
Speaker 2: going app ever, apparently, and gptwo four GPT four released
Speaker 2: in twenty twenty four was another step change, excelling at
Speaker 2: complex stuff, scoring in the ninetieth percentile on the bar exam.
Speaker 2: It showed AI tackling really high level analytical tasks.
Speaker 1: That's the sort of clo source big company side. What
Speaker 1: about the open source world that seems to be moving
Speaker 1: justice fast.
Speaker 2: Totally mis Lama series, Lama, Lama two, Lama three takes
Speaker 2: a different path by being open source that builds a
Speaker 2: whole community.
Speaker 1: Around it, democratizing it in a way exactly, and it
Speaker 1: allows for cool optimizations like quantization and Laura.
Speaker 2: Those are techniques to basically shrink or specialize these huge
Speaker 2: models so you can run them on like good home computer.
Speaker 1: GPU makes them more accessible. Any other big open source
Speaker 1: players Yeah.
Speaker 2: Mistral seven B from the French startup mistral ai is
Speaker 2: getting a lot of buzz too, another really powerful open
Speaker 2: source option. So right now, GPT four probably leads on
Speaker 2: raw capability in many areas, but open source like Lama
Speaker 2: and Mistral are super exciting, especially if you want to
Speaker 2: find you in a model for a very specific job.
Speaker 1: Okay, so we have these powerful models open and closed source,
Speaker 1: but how do developers actually build applications with them, connect
Speaker 1: them to data, make them do things. Is there a
Speaker 1: standard toolkit that's.
Speaker 2: Where frameworks like lang chain come in. It's become hugely popular.
Speaker 2: Is an open source framework Python and typescript designed specifically
Speaker 2: for building LM applications?
Speaker 1: Oh, what's its main goal?
Speaker 2: Two core ideas enhancing data awareness, connecting lms to external
Speaker 2: data they weren't trained on, and agency giving LMS the
Speaker 2: ability to take actions and influence their environment.
Speaker 1: Okay, data awareness and agency. How does it achieve that?
Speaker 2: Through modular building blocks things like model io for interacting
Speaker 2: with different models, retrieval for fetching data, chains for sequencing operations,
Speaker 2: agents for decision making, and tool use memory for remembering
Speaker 2: past interactions and callbacks for running code at certain points.
Speaker 1: Sounds comprehensive. Does it work with different AI providers?
Speaker 2: Yeah? Supports models from Anthropic, Google's Vertex Ai, OpenAI, and others.
Speaker 2: Plus it handles practical stuff like streaming, getting words back
Speaker 2: one by one like chat GPT does, and batching for
Speaker 2: running multiple requests in parallel.
Speaker 1: What about getting structured data out of the LM's responses reliably.
Speaker 2: That's where laying chain's output parts are key, especially the
Speaker 2: ones that use identic, which is great for defining Jason structures.
Speaker 2: They help reliably turn the AI's natural language answer into
Speaker 2: clean structured data. It essentially lets you build a flexible
Speaker 2: API on top of the LLM.
Speaker 1: And what about open AI's specific way for models to
Speaker 1: interact with external systems. Is that different?
Speaker 2: You're probably thinking of open AI function calling. It's their
Speaker 2: method for letting llms intelligently decide to call external functions.
Speaker 1: How does that work? Exactly?
Speaker 2: LLM analyzes the conversation, figures out it needs to do
Speaker 2: something specific, like check the weather. It then outputs a
Speaker 2: structured Jason object saying call a check weather function with
Speaker 2: location London. Your system runs that function, gets the weather data,
Speaker 2: feeds it back into the conversation, and the LLM can
Speaker 2: then summarize it for the user.
Speaker 1: So it tells your code what function to run and
Speaker 1: with what arguments.
Speaker 2: Very neat, very neat, Very powerful for integrations and for.
Speaker 1: Fine tuning the output on specific tasks, especially new ones.
Speaker 2: That brings us back to fu shot learning. Remember providing
Speaker 2: examples in the prompt.
Speaker 1: Yeah, like the ibar fridge example exactly.
Speaker 2: While zero shot relies just on the model's training, few
Speaker 2: shot gives it those crucial examples right in the prompt.
Speaker 2: It helps optimize the model's behavior for exactly what you want.
Speaker 2: It's like giving the AI a mini tutorial for the
Speaker 2: specific task.
Speaker 1: Does it still matter with models that have huge context windows? Now?
Speaker 2: Yeah, it often still helps Even with large context windows.
Speaker 2: A few good examples can guide the model to the
Speaker 2: right answer faster and more reliably, which can actually save
Speaker 2: you on API costs because you use fewer tokens overall
Speaker 2: to get the desired results.
Speaker 1: Okay, this is all incredibly powerful, but it raises a
Speaker 1: big question. How do we get these AI models to
Speaker 1: work securely and effectively with our data, our company knowledge,
Speaker 1: our specific documents, and how do we make them remember
Speaker 1: previous conversations that seems vital for real.
Speaker 2: World use, absolutely vital. This is where connecting llms to
Speaker 2: your data and managing memory really unlocks their practical potential.
Speaker 2: Let's talk data connection in VEC databases. Okay, so your
Speaker 2: organization's data. It comes in all shapes and sizes, right,
Speaker 2: unstructured stuff like Google docs, web pages, code and structure
Speaker 2: stuff in SQL. No SQL databases. To let the AI
Speaker 2: query that unstructured data, the process usually involves loading it
Speaker 2: into what Lang chain calls documents, then chunking them, breaking
Speaker 2: them into smaller pieces, and then storing these pieces in
Speaker 2: a special database called a vector database.
Speaker 1: Vector database Okay, what makes it special?
Speaker 2: It stores data based on meaning using embeddings. Embeddings are
Speaker 2: numerical representations vectors of text. Models like open aies text
Speaker 2: embedding ATA zero zero two or open source ones from
Speaker 2: hugging face turn text into these.
Speaker 1: Vectors, so numbers that represent the meaning exactly.
Speaker 2: Text with similar meanings end up closer together in this
Speaker 2: high dimensional mathematical space. Think of it like a map
Speaker 2: of concepts.
Speaker 1: Is creating these embeddings expensive.
Speaker 2: Actually, open ais are pretty cheap. The source mentioned embedding
Speaker 2: the entire King James Bible would cost something like a
Speaker 2: dollar sixty cents, and there are good open source options too.
Speaker 1: Okay affordable. So these embeddings go into the vector database.
Speaker 2: Right. Vector databases like FAES which is open source, or
Speaker 2: hosted ones like pine Cone or Chroma are built to
Speaker 2: store these vectors and search them based on semantic similarity,
Speaker 2: finding the vectors and thus the original text chunks that
Speaker 2: are closest in meaning to your query vector.
Speaker 1: And this whole process helps prevent the AI from just
Speaker 1: making things up right the hallucinations precisely.
Speaker 2: That leads us to retrieval augmented generation or R. This
Speaker 2: is the key technique for fighting hallucinations and also getting
Speaker 2: around those context length limits.
Speaker 1: How does our RAG work in practice?
Speaker 2: It's pretty elegant. A user asks a question, your system
Speaker 2: first converts that question into an embedding vector. Then it
Speaker 2: searches your vector database for the text chunks whose embeddings
Speaker 2: are most similar.
Speaker 1: Finds the relevant bits of your own data.
Speaker 2: Exactly, It retrieves those relevant jumps and literally inserts them
Speaker 2: into the prompt you sent to the LLM, providing explicit context. Then, crucially,
Speaker 2: you instruct the LLM to answer the user's question based
Speaker 2: only on the provided context.
Speaker 1: Ah. So you're forcing it to use your verified information,
Speaker 1: not just its general training data.
Speaker 2: Precisely, it lets you dynamically pull in specific up to
Speaker 2: date knowledge, maybe chat history, specific PDS, sections, products, pecs,
Speaker 2: ensuring the AI's answer is informed, relevant, and grounded in fact.
Speaker 1: That's huge for accuracy. Okay, so gig gives it factual knowledge.
Speaker 1: What about memory, making it remember past parts of a
Speaker 1: conversation or user preferences over time.
Speaker 2: That's memory in llms, and it's crucial for making interactions
Speaker 2: feel natural and personalized. We can think about two types. First,
Speaker 2: short term memory.
Speaker 1: STM like working memory kind of Yeah, it lets.
Speaker 2: The LLM remember what was said earlier within the same interaction.
Speaker 2: Think of a support chatbot remembering your initial query when
Speaker 2: you ask a follow up question minutes later.
Speaker 1: Lane chain makes adding STM pretty straightforward.
Speaker 2: Okay, remembers the current chop. What about remembering things across
Speaker 2: different sessions days or weeks later.
Speaker 1: That's long term memory LTM, and this is usually achieved
Speaker 1: by storing summaries of past conversations or key pieces of
Speaker 1: information in a vector database. When the user starts a
Speaker 1: new session, you retrieve relevant past interactions or preferences using
Speaker 1: similarity search and add that as context to the prompt,
Speaker 1: so it.
Speaker 2: Can remember my book preferences from last month when I
Speaker 2: ask for new recommendation exactly.
Speaker 1: That it allows for truly personalized, context aware interactions over time.
Speaker 1: This is where it starts to feel really intelligent, capable
Speaker 1: of complex tasks, which brings us to AI agents. What
Speaker 1: if the AI could not just think or retrieve info,
Speaker 1: but actually do things, take actions exactly.
Speaker 2: We're now in the realm of agent based architecture. The
Speaker 2: AI acts, perceives, makes decisions to achieve goals. A key
Speaker 2: technique enabling this is chain of thought ski B reasoning.
Speaker 1: We touched on that, making the AI think step by
Speaker 1: step right.
Speaker 2: Instead of just asking for a say a marketing plan.
Speaker 2: You prompt the AI to first think through the steps.
Speaker 2: First consider the target audience, Second, analyze the budget constraints,
Speaker 2: third research competitor products. Then outline the.
Speaker 1: Plan breaking down the problem. Yeah.
Speaker 2: It forces a more structured reasoning process, leading to much
Speaker 2: more relevant and well thought out responses than just asking
Speaker 2: for the final answer directly. It's like making it show
Speaker 2: its work.
Speaker 1: Okay, so copey improves reasoning. How does that connect to
Speaker 1: taking actual actions?
Speaker 2: That leads directly to the reason and act REACT framework.
Speaker 2: This explicitly combines that chain of thought reasoning with the
Speaker 2: ability to take actions using tools.
Speaker 1: Okay, reason and act. How does that loop work?
Speaker 2: It's a cycle. One thought. The LM internally reasons about
Speaker 2: what it needs to do. Next. Two action, It decides
Speaker 2: which tool it needs to use, like a search engine
Speaker 2: or a calculator, and formulates the input for that tool.
Speaker 2: Three observation. It receives the result from the tool. This thought,
Speaker 2: action observation continues until it reaches the final answer or
Speaker 2: completes its task.
Speaker 1: So it can decide, I need to search the web
Speaker 1: for this. Run the search tools, see the results, then
Speaker 1: think about the next step based on those results.
Speaker 2: Processing. It allows the AI to interact dynamically with its
Speaker 2: environment to gather information or perform tasks.
Speaker 1: What kind of tools can these agents use? Are they
Speaker 1: pre defined?
Speaker 2: Yes, they're pre defined functions or APIs you may available
Speaker 2: to the agent. Examples are things like a simple calculator,
Speaker 2: a Google Search interface, tools to interact with your file
Speaker 2: system read write files, tools to make HTTP requests like
Speaker 2: interacting with web APIs, or even things like Twilio to
Speaker 2: send SMS messages.
Speaker 1: So you equip the agent with the capabilities it needs.
Speaker 2: Exactly and a key tip from the book, give your
Speaker 2: tools really clear descriptive names and descriptions. It significantly helps
Speaker 2: the LM choose the right tool for the job. Lang
Speaker 2: Chain also offers pre built agent toolkits for common scenarios,
Speaker 2: like a CSV agent that can query data in spreadsheets
Speaker 2: or a SEQL agent for databases. Saves you building everything
Speaker 2: from scratch makes sense.
Speaker 1: Are there even more advanced agent designs out there going
Speaker 1: beyond this React loop?
Speaker 2: Oh? Yeah, we're seeing advanced agent architectures emerge. One example
Speaker 2: is baby Agi. It's less a single loop and more
Speaker 2: a system of interacting agents.
Speaker 1: How does babyagi work?
Speaker 2: It has a continuous cycle. It pulls a task from
Speaker 2: a list, uses an LLM in context, often from a
Speaker 2: vector dB like Chroma or weaviate, to execute. It saves
Speaker 2: a result. Then a separate task creation agent figures out
Speaker 2: new follow up tasks based on the outcome, and another
Speaker 2: prioritization agent reorders the task list. It's a self perpetuating
Speaker 2: goal seeking system.
Speaker 1: Wow, like a little automated project manager kind of Another
Speaker 1: fascinating one is Tree of Thoughts TUT.
Speaker 2: This moves beyond simple linear, step by step.
Speaker 1: Reasoning Tree of thoughts. How's that different?
Speaker 2: It lets the LLM explore multiple potential reasoning paths simultaneously.
Speaker 2: Like branches on a tree. You can evaluate different paths,
Speaker 2: backtrack if one isn't working, and explore alternatives, much more
Speaker 2: like human brainstorming or strategic thinking.
Speaker 1: Does it actually improve performance.
Speaker 2: Significantly on certain types of problems? The example given is
Speaker 2: the game of twenty four puzzle standard GPS four. With
Speaker 2: just chain of thought got it right maybe four percent
Speaker 2: of the time. With Tree of Thoughts exploring different calculation paths,
Speaker 2: it jumped to seventy four percent success.
Speaker 1: That's a massive difference. Shows the power of exploring multiple options.
Speaker 2: A huge difference really pushes the boundaries of AI problem solving.
Speaker 1: Okay, switching gears a bit, but still on this creative
Speaker 1: potential image generation For a lot of people. That was
Speaker 1: the wow moment for AI. It feels like magic, But
Speaker 1: how do we actually guide that process effectively with prompts?
Speaker 2: It definitely feels like magic, But yeah, prompt engineering is
Speaker 2: your magic wand here the technology behind most of it
Speaker 2: is diffusion model.
Speaker 1: Diffusion models right introduced back.
Speaker 2: In twenty fifteen, but they really took off recently. They
Speaker 2: produced amazing images from text descriptions. You know the big names.
Speaker 2: Two came out in twenty twenty two, Mid Journey hit
Speaker 2: the scene July twenty twenty two, and then open source
Speaker 2: Stable Diffusion landed August twenty twenty two, and Dali three
Speaker 2: is now baked into chat Gypt.
Speaker 1: How do they actually work?
Speaker 2: In simple terms, They're trained on billions of image caption pairs.
Speaker 2: They learn the connection between words and visuals. The process
Speaker 2: starts with random noise like TV static, and they gradually
Speaker 2: denois it step by step, guiding it towards an image
Speaker 2: that matches your text prompt. They navigate this huge latent space.
Speaker 2: Think of it as a vast map of all possible
Speaker 2: images to find the spot that matches your description.
Speaker 1: Fascinating What are the sort of vibes or strengths of
Speaker 1: those main models? Dowly, mid Journey, Stable Diffusion.
Speaker 2: Well Della got famous for its artistic flexibility, though early
Speaker 2: versions sometimes struggled with like hands and eyes, mid Journey
Speaker 2: built a huge following, especially on Discord, known for its
Speaker 2: distinct esthetic, often fantasy sci fi, very polished photorealistic styles.
Speaker 2: Stable Diffusion really shook things up by being open source.
Speaker 2: Want it yourself right, Yeah, run around your own computer
Speaker 2: if you have a decent graphics card. That open nature
Speaker 2: led to super fast development, tons of community add ons
Speaker 2: in advanced features like control Net. It's generally seen as
Speaker 2: the most fleshxible and extendable one.
Speaker 1: So how do our basic prompting principles direction format examples
Speaker 1: apply to making images?
Speaker 2: They apply surprisingly well. First format modifiers, Just like specifying
Speaker 2: JSON for text, you specify the visual style an oil
Speaker 2: painting of a business meeting versus an ancient Egyptian hieroglyph
Speaker 2: of a business meeting. Change in the whole look completely.
Speaker 2: Just be aware sometimes the style brings baggage from the
Speaker 2: training data like oil paintings often appearing with digital frames
Speaker 2: around them unless you negate that.
Speaker 1: Oh okay, what about specific artists.
Speaker 2: Yep art style modifiers. You can ask for the style
Speaker 2: of Van Go Dolly, Picasso, specific art movements. Mid Journey
Speaker 2: even has a described command where you upload an image
Speaker 2: and it suggests prompts that might create something similar. Great
Speaker 2: for learning cool.
Speaker 1: Are there quick tricks to just make the images look
Speaker 1: better higher quality?
Speaker 2: Yeah? Quality boosters simple words like four K, highly detailed
Speaker 2: masterpiece trending on art station. Adding these often bumps up
Speaker 2: the quality without drastically changing the content or style.
Speaker 1: Easy wins. And what about telling it what not to include?
Speaker 2: Crucial? That's negative prompts You specify what you don't want,
Speaker 2: like for that oil painting, you might add no frame
Speaker 2: border signature, or if you want a realistic Homer Simpson,
Speaker 2: you'd add no cartoon animation. It helps untangle concepts the
Speaker 2: AI might merge and fixes common glitches like mangled hands.
Speaker 1: Very useful. Can you emphasize certain parts of the prompt?
Speaker 2: Yes, using weighted terms. Different models have different syntax, but
Speaker 2: often you use parentheses, maybe within a number like hyropol
Speaker 2: one point five to boost its importance or square brackets
Speaker 2: hat in some systems to deemphasize something gives you finer control.
Speaker 1: Okay, what if you have an image you like and
Speaker 1: want something similar.
Speaker 2: That's prompting with an image often called MG two img.
Speaker 2: You provide a starting image as guidance the AI tries
Speaker 2: to capture its vibe, composition, or style. It's like a
Speaker 2: one shot visual example for.
Speaker 1: The AI need. What about editing images the AI creates
Speaker 1: or keeping care aracters consistent across multiple images. That seems hard.
Speaker 2: It is tricky, but there are tools. In painting lets
Speaker 2: you mask off a specific area of an image and
Speaker 2: then prompt the AI to regenerate just that area, like
Speaker 2: changing someone's shirt color or adding.
Speaker 1: An object to targeted changes exactly.
Speaker 2: And outpainting does the opposite. It extends the image beyond
Speaker 2: its original borders, generating new content that fits. People often
Speaker 2: use a combination of in painting and outpainting to try
Speaker 2: and maintain consistent characters across a series of images. Tweaking
Speaker 2: faces or outfits as needed.
Speaker 1: Still sounds a bit manual. Are there more advanced ways
Speaker 1: to control the composition or pose?
Speaker 2: Oh? Yes, this is where control net comes in especially
Speaker 2: for stable diffusion. It's a game changer. It lets you
Speaker 2: provide an input image purely for structural guidance, things like
Speaker 2: canny edge maps, depth maps, human post skeletons from open pos,
Speaker 2: segmentation maps, even just rough.
Speaker 1: Scribbles so you could sketch a layout and control NET
Speaker 1: makes the AI follow that structure precisely.
Speaker 2: It gives artist's incredible control over the final composition while
Speaker 2: letting the AI handle the rendering in details. It bridges
Speaker 2: the gap between human intent and AI generation. Another helpful
Speaker 2: tool is the segment Anything Model SAM from Meta AI.
Speaker 2: It's amazing at precisely identifying and masking objects or people
Speaker 2: in an image, which is super useful for targeted in painting.
Speaker 1: Wow, that's real control. What about teaching the AI about
Speaker 1: my specific product, or my face or my company style
Speaker 1: stuff it wasn't trained on.
Speaker 2: That's personalization and the main technique there is green booth
Speaker 2: fine tuning. You teach the diffusion model a new concept
Speaker 2: by showing it just a few images of that thing,
Speaker 2: your product, your pet, whatever. It creates a new custom
Speaker 2: model file that understands that specific concept.
Speaker 1: So I can generate images of my dog in ben
Speaker 1: Go's style.
Speaker 2: Exactly that kind of thing, and tying this all together.
Speaker 2: There's meta prompting for images.
Speaker 1: Using one AI to write the prompt for another.
Speaker 2: Yeah, you could ask chat GPT rate me a detailed
Speaker 2: bid journey prompt for a photorealistic image of a few
Speaker 2: uturistic city scape at sunset. It often crafts a much better,
Speaker 2: more detailed prompt than a non expert might write themselves,
Speaker 2: divides the labor effectively.
Speaker 1: Clever and how what was that last really intriguing concept meme?
Speaker 2: Something meme unbundling and mapping. This is more advanced conceptual stuff.
Speaker 2: Instead of just copying an artists like in the style
Speaker 2: of Van Go, you try to decompose that style into
Speaker 2: its core components, the memes, meaning the recurring visual elements
Speaker 2: color palettes, breashtrobe types, compositional tricks.
Speaker 1: Break down the style into its ingredients exactly.
Speaker 2: Then you can remix those ingredients, maybe combining elements from
Speaker 2: different styles to create something new and original. Meme mapping
Speaker 2: is about the community, aspect sharing, analysis, learning from successful prompts,
Speaker 2: figuring out together what makes certain styles visually appealing. It's
Speaker 2: about deconstructing and reconstructing visual language.
Speaker 1: Fascinating Okay, you've walked us through an incredible range of techniques,
Speaker 1: from basic text prompts to complex agents and highly controlled
Speaker 1: image generation. How does this all come together? Can you
Speaker 1: give us a practical example of building something real with
Speaker 1: these techniques.
Speaker 2: Sure, Let's imagine building an end to end AI blog
Speaker 2: writing system. This would integrate many things we've discussed.
Speaker 1: Okay, an automated blog writer. How would it start?
Speaker 2: First? Topic research? It could use a tool maybe integrated
Speaker 2: via lang chain like Google search results, to scrape top
Speaker 2: Google hits for the chosen topic. Process that info to
Speaker 2: get a baseline understanding.
Speaker 1: So grounded in actual search data. Smart.
Speaker 2: Then to make the content unique, it could simulate an
Speaker 2: expert interview. You'd use role prompting to have one LLLM
Speaker 2: act as an expert on the topic. Another LLM interview
Speaker 2: it generating unique insights and quotes you wouldn't find just
Speaker 2: by scraping the web.
Speaker 1: Adding original perspective. Nice. What's next?
Speaker 2: Outline generation? The system would prompt an LLM to create
Speaker 2: a detailed, structured outline, maybe in Jason or nested list format,
Speaker 2: based on the research in the interview.
Speaker 1: Okay, structure first, then the writing.
Speaker 2: Right, text generation It would go section by section through
Speaker 2: the outline, feeding the LLM the relevant research chunks and
Speaker 2: interview snippets as context for each part, with strict instructions
Speaker 2: not to plagiarize. This relies heavily on good chunking and
Speaker 2: contextual prompting makes sense.
Speaker 1: What about images for the blog.
Speaker 2: Post image generation? For each post, it could generate a
Speaker 2: custom image. This could be a two step process using
Speaker 2: meta prompting. First, have an LM generate a really good
Speaker 2: descriptive image prompt based on the section's content.
Speaker 1: The AI writes the image prop exactly.
Speaker 2: Then feed that prompt to an image model like stable
Speaker 2: Diffusion XL. You could even specify a consistent style like
Speaker 2: corporate Memphis for all images across the blog fully automated
Speaker 2: visuals Wow.
Speaker 1: Any optimization after the content is.
Speaker 2: Written definitely title optimization. Use an LLM to generate or
Speaker 2: refine the title for better SEO and clickser rates, and
Speaker 2: crucially rewriting for.
Speaker 1: Style matching a specific brand voice precisely.
Speaker 2: The system could take generated draft and rewrite it to
Speaker 2: match a defined style like informative and analytical with practical
Speaker 2: actionable advice. This could be tricky, often needing a powerful
Speaker 2: model like GPT four and careful prompt tuning, maybe ab
Speaker 2: testing against human examples.
Speaker 1: Seems like that style part could be the.
Speaker 2: Hardest often is. And finally, for getting it out there
Speaker 2: quickly user interface, you wouldn't need a complex web app
Speaker 2: right away. You could build a simple prototype using Python
Speaker 2: libraries like radio or streamlet, just to get it working
Speaker 2: and gather early feedback.
Speaker 1: So a full workflow from research to style, texts and
Speaker 1: images all orchestrated using these AI techniques. That's impressive.
Speaker 2: It really shows how these components are agents, prompting techniques,
Speaker 2: structured data generation can stack together to build something powerful.
Speaker 1: Wow. That was an incredible journey. Seriously, from the absolute
Speaker 1: basics of a good prompt all the way to building
Speaker 1: automated systems and creating custom art. You've given us a
Speaker 1: proper deep dive into what's actually possible right now.
Speaker 2: It's amazing to see how it all connects, isn't it.
Speaker 2: Specifying formats, using vector databases, for memory, agents, taking actions,
Speaker 2: fine tuning models, They're all pieces of this bigger puzzle
Speaker 2: for creating reliable and frankly intelligent AI applications. Things that
Speaker 2: felt like science fiction just a couple of years back.
Speaker 1: So for everyone listening, what's the big takeaway? What does
Speaker 1: this mean for you? I think it means that interacting
Speaker 1: with AI isn't just about typing a quick query anymore.
Speaker 1: It's about realizing you have the controls exactly.
Speaker 2: You have the power to direct these models, to refine
Speaker 2: their output, even to teach them new things, to get
Speaker 2: incredibly specific, high quality results that are personalized to your needs.
Speaker 1: You move from being just a user to being more
Speaker 1: like a director or a collaborator with the AI.
Speaker 2: That's a great way to put it. Yeah, the challenge
Speaker 2: now really is to take these ideas and experiment. Think
Speaker 2: about your own work, your own creative projects. How could
Speaker 2: combining some of these techniques maybe RAG with your company's data,
Speaker 2: or an agent to automate a tedious task, or finally
Speaker 2: too image generation. How could that solve your unique problems?
Speaker 1: Where could you apply this power to transform how you
Speaker 1: work or unlock something totally new?
Speaker 2: That's the question to ponder.
Speaker 1: That really is a great thought to leave everyone with.
Speaker 1: This Deep dive has given us a fantastic practical toolkit.
Speaker 1: Thanks so much for walking us through it.
Speaker 2: My pleasure. It's an exciting field.
Speaker 1: And thanks to you for joining us on the deep dive.
Speaker 1: We genuinely hope this empowers you to go out and
Speaker 1: become a master of your own AI craft.