Speaker 1: So imagine this, What if you could talk to AI, like,
Speaker 1: really talk to it so it just gets exactly what
Speaker 1: you mean and it responds perfectly.
Speaker 2: Yeah, like a genuine meeting of minds, almost exactly.
Speaker 1: Well, today we're diving deep into something called prompt engineering.
Speaker 1: It's basically the art and science of having those kinds
Speaker 1: of conversations with large language models, you know lms like chat, GPT,
Speaker 1: and it's way more than just typing in a question, right,
Speaker 1: It's about really crafting that dialogue, it really is.
Speaker 2: And our guide for this is a fascinating new book
Speaker 2: just out in twenty twenty five. It's called Prompt Engineering
Speaker 2: Empowering Communication, Oh okay, by Ajantha Devi Varamani and Anan Naire,
Speaker 2: and it's well, it's pretty foundational. It's like our map
Speaker 2: for understanding how to really tap into what these lms
Speaker 2: can do.
Speaker 1: Okay, great, So our mission today for you listening is
Speaker 1: basically to cut through the hype maybe some of the
Speaker 1: jargon too, Yeah, definitely, and get you really informed about
Speaker 1: what prompt engineer is, why it suddenly becomes so important,
Speaker 1: and you know how you might start thinking about using
Speaker 1: it even if you're totally new to this, right, we'll
Speaker 1: look at some maybe surprising ways prompts are being used
Speaker 1: and what it all kind of means for how we'll
Speaker 1: interact with AI going forward. That's good, okay, So let's
Speaker 1: unpack this a bit. We've all like asked chat chepte
Speaker 1: a question, yeah, or use some other AI.
Speaker 2: Tool, Sure most people probably have by now.
Speaker 1: But prompt engineering it takes that simple question and turns
Speaker 1: it into something well more sophisticated. The book has this
Speaker 1: great description actually, oh yeah. It calls it intertwining instructions
Speaker 1: and information into this web in a way that gently
Speaker 1: nudges the LM toward the intended result.
Speaker 2: Nudges I like that.
Speaker 1: Yeah. It says it's like whispering ideas into the ear
Speaker 1: of a gifted artist, offering just enough direction to ignite
Speaker 1: their creative spark without drowning out their distinct voice.
Speaker 2: That's a fantastic analogy. It captures that subtlety. It's not
Speaker 2: just commanding, it's.
Speaker 1: Guiding exactly, moving from just asking to actually guiding the AI.
Speaker 2: And that means the prompt itself changes meaning, doesn't it.
Speaker 2: It's not just your question anymore. It becomes the whole package,
Speaker 2: the instructions. Maybe an example, you give it the background context.
Speaker 2: It's the roadmap you provide for the AI's answer.
Speaker 1: So it could be just attendance or much more complex.
Speaker 2: Oh, absolutely, it could be quite detailed, several clauses, specific directives.
Speaker 2: You're influencing how it responds using natural language, or sometimes
Speaker 2: even specific system instructions or constraints. The art really is
Speaker 2: in crafting that guidance effectively.
Speaker 1: Okay, so why now? Why is this suddenly such a
Speaker 1: big deal? What are the implications here?
Speaker 2: That really is the core question, isn't it? And the
Speaker 2: book lays out a few key reasons why prompt engineering
Speaker 2: is vital right now?
Speaker 1: Okay.
Speaker 2: Firstly, it helps us unlock the full potential of these elms.
Speaker 2: Think about it. These models have learned from just unimaginable
Speaker 2: amounts of text data.
Speaker 1: Right the scale is huge.
Speaker 2: Exactly, and prompt engineering lets us tap into that knowledge
Speaker 2: for things they weren't explicitly trained to do. The book
Speaker 2: mentions things like authoring engrossing novels or complex musical compositions.
Speaker 1: Wow, okay, so pushing beyond their standard programming.
Speaker 2: Precisely extending what they can inherently do. Secondly, it gives
Speaker 2: us precision and management. This is crucial. How so, Well,
Speaker 2: if you need accuracy, think about news reporting or scientific study,
Speaker 2: you can't just have a vague answer. Prompt engineering lets
Speaker 2: you carefully craft the output guide it towards factual accuracy,
Speaker 2: towards the specific requirements of the task.
Speaker 1: So getting exactly what you need, not just something exactly.
Speaker 2: Then there's uniqueness and creativity. A really well designed prompt
Speaker 2: can spark the llms well, you could call it imagination,
Speaker 2: leading to novel and surprising results. It can genuinely help
Speaker 2: foster creative solutions that you might not have thought of otherwise.
Speaker 1: Okay, that's interesting using it as a creative partner.
Speaker 2: Yeah. And finally, and this is a big one, it's
Speaker 2: about democratizing AI. Good prompt engineering makes these powerful tools
Speaker 2: more accessible.
Speaker 1: How does that work?
Speaker 2: Well, people without deep technical coding skills can still leverage
Speaker 2: AI effectively. They can use prompts to get the AI
Speaker 2: to do complex things for their creative and productive efforts.
Speaker 2: It opens it up right, so.
Speaker 1: It's not just for developers anymore, artists, writers, small businesses, everyone.
Speaker 2: Potentially, it fundamentally changes who gets to innovate with this tech.
Speaker 1: It's easy to think this is all brand new, like
Speaker 1: AI just appeared. But you're saying the roots of this
Speaker 1: guiding AI with language go back further.
Speaker 2: Oh much further. You're right. The idea isn't new at all.
Speaker 2: If you connect the dots, you can trace it back
Speaker 2: to early experiments like what well think about chatbots like
Speaker 2: Eliza that was way back in nineteen sixty six. Wow,
Speaker 2: it mimicked a therapist trying to hold a conversation. And
Speaker 2: then in the seventies there was shr dlu shrdlu. Yeah,
Speaker 2: it was a natural language system that could understand and
Speaker 2: follow commands in a simple virtual world of blocks.
Speaker 1: So early attempts at language based in st diction.
Speaker 2: Exactly rudimentary, is sure, but they lay the groundwork. They
Speaker 2: showed that human language could direct what an AI does.
Speaker 1: And then things really took off with the transfermer revolution
Speaker 1: in twenty seventeen. That seems like the pivotal moment.
Speaker 2: Absolutely, that paper attention is all you need was the breakthrough.
Speaker 2: It paved the way for these incredibly powerful lms like
Speaker 2: GPT three, and that's when prompt engineering really emerged as
Speaker 2: its own distinct skill, its own field.
Speaker 1: Right, and the open AI GPT models that progression is
Speaker 1: pretty key to understanding this isn't it?
Speaker 2: It really is. It shows the rapid evolution. So GPT
Speaker 2: one came out in twenty eighteen. It used unsupervised pre training,
Speaker 2: had about one hundred and seventeen million parameters.
Speaker 1: Parameters those like the internal knobs the AI uses.
Speaker 2: To learn sort of yeah, the variables. It adjusts a
Speaker 2: huge number of them. Then GPT two in twenty nineteen
Speaker 2: was a big step up one point five billion parameters
Speaker 2: WHOA and trained on much more data about forty gigabytes.
Speaker 2: It showed definite improvements in coherence generating texts.
Speaker 1: GPT three was the next massive.
Speaker 2: Leap massive is the word twenty twenty one hundred and
Speaker 2: seventy five billion parameters trained on huge chunks of the
Speaker 2: Internet common crawl Wikipedia, and it showed really impressive coherence
Speaker 2: and creativity. It could even write code, which was pretty
Speaker 2: mind blowing at the time.
Speaker 1: But it wasn't perfect. Was it? Their issues?
Speaker 2: No, definitely not. The book points out challenges with misalignment.
Speaker 2: Sometimes what the user wanted didn't quite line up with
Speaker 2: how the model was trained. I mean, it could generate
Speaker 2: stuff that was wrong, erroneous, or even problematic content like
Speaker 2: biased are toxic text. That was a real hurdle.
Speaker 1: So how do they tackle that? Because that sounds like
Speaker 1: a major barrier to using it widely.
Speaker 2: It was, and OpenAI solution was pretty ingenious. Actually, they
Speaker 2: introduced something called instruct GPT in twenty twenty one.
Speaker 1: Instruct GPT, Yeah, and the.
Speaker 2: Key was using reinforcement learning with human feedback our LHF.
Speaker 1: Okay, what does that involve.
Speaker 2: Well, it's quite meticulous. They had human labelers write high
Speaker 2: quality ideal answers to various.
Speaker 1: Prompts I like examples of good behavior exactly.
Speaker 2: And then they trained another model, a reward model, to
Speaker 2: basically score the AI's responses based on how close they
Speaker 2: were to those human examples.
Speaker 1: So the AI learned to aim for what the humans
Speaker 1: preferred precisely.
Speaker 2: It iteratively refined the main model, and the book says
Speaker 2: this significantly enhances model accuracy and reduces the generation of
Speaker 2: toxic content, made them much safer and more reliable.
Speaker 1: And that whole process, that refinement led directly to the
Speaker 1: models we know now pretty much.
Speaker 2: That work fed into the GPT three point five series,
Speaker 2: and then, of course November twenty twenty two, Chat GPT
Speaker 2: launched based on that series.
Speaker 1: Right when it really hit the mainstream.
Speaker 2: Yeah, and then March twenty twenty three we got GPT
Speaker 2: four the current top.
Speaker 1: Tier, and that one's multimodal right handles images too, that's
Speaker 1: the claim.
Speaker 2: Yes, the inaugural multimodal model. It can process text and images,
Speaker 2: though the image input part isn't widely public yet.
Speaker 1: But the performance jumps huge.
Speaker 2: Oh staggering. The book gives examples like the Uniform Bar
Speaker 2: Exam CHAT GPT scored around the tenth percentile GPT four
Speaker 2: ninetieth percent style or the International Biology Olympiad CHAT GPT
Speaker 2: was thirty first percentile GTT four hit the ninety ninth.
Speaker 1: That's incredible progress in just a year or so.
Speaker 2: It really highlights how fast this field is moving.
Speaker 1: Okay, so we've got the history, we know what's kind
Speaker 1: of under the hood, how do we actually do prompt engineering? Like,
Speaker 1: for you listening, what are the practical ways to start
Speaker 1: guiding these ais better?
Speaker 2: Right? The nuts and bolts? Well, the book lays out
Speaker 2: a whole toolkit, but the core principles are pretty straightforward. Okay.
Speaker 2: It starts with really understanding the task you want the
Speaker 2: AI to do, being precise and succinct in your language,
Speaker 2: giving enough detail but not too much.
Speaker 1: Finding that balance.
Speaker 2: Exactly providing examples can be huge and maybe most importantly, experiment.
Speaker 2: There's no single magic formula. It's iterative. You try something,
Speaker 2: see what happens.
Speaker 1: To Okay, So let's dig into some specific techniques people
Speaker 1: can try. The book mentions, the instructions prompt technique first
Speaker 1: sound straightforward.
Speaker 2: It is pretty much you basically just tell the model
Speaker 2: exactly what you want it to do, often using clear
Speaker 2: commands imperative verbs. So yeah, so if you need customer
Speaker 2: service replies, you might instruct it. Responses should be professional
Speaker 2: and provide accurate information, nice and clear.
Speaker 1: Or for maybe a legal context.
Speaker 2: Yeah, you could say draft this section ensuring the document
Speaker 2: complies with relevant laws and regulations. You're setting clear rules
Speaker 2: guardrails for the output.
Speaker 1: Okay, makes sense. Then there's zero one and few shot prompting.
Speaker 1: That sounds interesting. What's the difference?
Speaker 2: This is a really helpful way to think about context.
Speaker 2: So zero shot is when you give it no specific
Speaker 2: examples in the prompt itself, just the instruction, just the instruction. Yeah,
Speaker 2: like write a poem about nature, the AI has to
Speaker 2: rely entirely on its pre existing knowledge. It's general training
Speaker 2: about poems and nature, so.
Speaker 1: It's kind of gets based on its vast training data.
Speaker 2: In a way. Yes, and the book points out that
Speaker 2: while it sounds simple, every word counts massively here. A
Speaker 2: slightly ambiguous word can send it off track. It really
Speaker 2: tests your ability to be concise and clear.
Speaker 1: Okay, so what if you need to give it a
Speaker 1: bit more direction, show what you mean.
Speaker 2: That's where one shot and FU shot come in. With
Speaker 2: one shot, you provide just one example.
Speaker 1: Uh So, say you.
Speaker 2: Want a paragraph summarized in a very specific, concise style.
Speaker 2: You'd give it the paragraph and then maybe one example
Speaker 2: of a sentence summarizing the style you want. You're showing
Speaker 2: it how just.
Speaker 1: Once demonstrating the task exactly.
Speaker 2: And FU shot just scales that up a bit. You
Speaker 2: give it a small number maybe two, three, five examples.
Speaker 1: Like for a product review, Yeah.
Speaker 2: Good example. If you want to review with certain elements,
Speaker 2: you might describe the product briefly, then provide maybe two
Speaker 2: or three examples of well written reviews formatted the way
Speaker 2: you like. This gives the AI more patterns to.
Speaker 1: Learn from, and that leads to better, more tailored results.
Speaker 2: Usually often, Yes, especially for more or complex or stylistic tasks,
Speaker 2: more examples generally mean better guidance.
Speaker 1: Okay, And the last one mention here is self consistency prompt.
Speaker 1: What's that about? Sounds important for factual stuff? It is.
Speaker 2: It's crucial when you need the output to be logical
Speaker 2: and coherent, and especially when it needs to stick to
Speaker 2: certain facts or principles.
Speaker 1: How does it work?
Speaker 2: You basically embed key facts or maybe core principles directly
Speaker 2: within the prompt itself.
Speaker 1: Ah, so you feed it the essential info it needs
Speaker 1: to stay.
Speaker 2: On track precisely. The book uses the example of a
Speaker 2: climate change discussion. You might include specific established scientific data
Speaker 2: points or consensus statements right in the prompt, and that.
Speaker 1: Helps prevent it from just making things up or going
Speaker 1: off on a tangent.
Speaker 2: Exactly. It acts like an internal reference guide for that
Speaker 2: specific answer, keeping it grounded in the information you provided.
Speaker 1: These techniques they really seem to allow for incredibly specific outcomes,
Speaker 1: don't they Moving way beyond just generic answers?
Speaker 2: They really do. It's about tailoring the AI's as capabilities
Speaker 2: to your precise needs. And this is why the future
Speaker 2: of LLM communication looks so interesting. Things like AI powered
Speaker 2: negotiation tools or real time translation that understands nuance or
Speaker 2: news updates customize just for you. Prompt engineering is the
Speaker 2: key to unlocking all of that. It's how we teach
Speaker 2: the AI to understand our intent with much, much greater precision.
Speaker 1: You know, the book title Empowering Communication really starts to
Speaker 1: make sense. Now. It's not just about tech, is it.
Speaker 1: These prompts can actually change how we work and interact
Speaker 1: across well, pretty much everywhere.
Speaker 2: That's a really important point. The book actually highlights applications
Speaker 2: in over a dozen different professional fields. It's incredibly versatile.
Speaker 1: Give us some examples, maybe some less obvious ones for
Speaker 1: people listening to think about.
Speaker 2: Okay, sure, well, take creative thinking prompts can genuinely help
Speaker 2: unlock imagination and innovation. How so imagine using prompts designed
Speaker 2: specifically to challenge your assumptions during brainstorming, or prompts that
Speaker 2: go an AI to generate, say, an initial sketch for
Speaker 2: an artwork representing the beauty of diversity and inclusivity. It's
Speaker 2: a starting point for human creativity.
Speaker 1: So using the AI not just to do the creative work,
Speaker 1: but to spark our own creativity.
Speaker 2: That's fascinating exactly, or think about effective writing. We all
Speaker 2: face writer's block sometimes, right prompts can be invaluable for
Speaker 2: just igniting the writing process. You could ask for a
Speaker 2: prompt to explore the concept of time travel and a
Speaker 2: short story, but maybe from the perspective of an object
Speaker 2: not a person, or create a story about a character
Speaker 2: who discovers a hitting world within everyday objects. It gives
Speaker 2: you that initial seed, a narrative launch pad precisely, and
Speaker 2: then in business, prompts can help craft effective presentations, maybe
Speaker 2: generate different options for an opening hook, like a powerful
Speaker 2: rhetorical question to grab the audience, or even assist in
Speaker 2: negotiation and persuasion by helping you frame arguments about, say
Speaker 2: the importance of building rapport before diving into the deal points.
Speaker 1: And it reaches into really specialized fields too, like medicine
Speaker 1: or law.
Speaker 2: Absolutely the book mentions healthcare professionals using prompts to enhance
Speaker 2: empathetic patient communication, maybe prompts that encourage patients to articulate
Speaker 2: their health goals clearly or guiding ethical decision making discussions.
Speaker 2: And for legal professionals, prompts could help draft sections of
Speaker 2: persuasive legal writing. Maybe exploring arguments for or against the
Speaker 2: constitutionality of a law to help refine the lawyer's own thinking.
Speaker 1: Okay, and data science, that seems like a natural fit.
Speaker 2: Definitely for data scientists. Prompts can guide analysis at every stage,
Speaker 2: from framing questions for data cleaning and preprocessing, like asking
Speaker 2: the AI to reflect on the importance of data cleaning,
Speaker 2: all the way to generating different types of data visualizations
Speaker 2: or summarizing findings from complex models.
Speaker 1: Yeah, that's a huge range. It really drives home that
Speaker 1: this isn't just a niche tech skill, it's becoming a
Speaker 1: fundamental communication skill across disciplines.
Speaker 2: I think that's exactly right. It's about empowering community, whoever
Speaker 2: you are, whatever field you're in.
Speaker 1: So we've talked a lot about using prompts as an
Speaker 1: individual user interacting with chat GPT on the website or
Speaker 1: similar But what if you're a developer or a business,
Speaker 1: What if you want to build these AI capabilities into
Speaker 1: your own software, your own products. That's where the API comes.
Speaker 2: In, right exactly. The chat GPT API Application Programming Interface
Speaker 2: is what lets developers integrate these powerful conversational AI features
Speaker 2: directly into their own apps and services.
Speaker 1: The bridge. Basically, it's the bridge.
Speaker 2: It moves beyond just the chat window and lets you
Speaker 2: embed the AI's power systematically.
Speaker 1: Okay, so for someone listening, why would they care about
Speaker 1: using an API versus just going to the website. What
Speaker 1: are the big advantages?
Speaker 2: Well, they're pretty significant. First, you can build much more
Speaker 2: customized conversational interfaces, think chatbots or virtual assistance embedded right
Speaker 2: into your product, speaking your brand's voice, accessing your specific data.
Speaker 2: Much more integrated, right, not just a generic bot exactly. Second,
Speaker 2: content generation at scale. The API lets you automate the
Speaker 2: creation of marketing, copy, articles, summaries, whatever you need, much faster,
Speaker 2: and ensuring a consistent tone and style across everything.
Speaker 1: That could be huge for businesses.
Speaker 2: Oh absolutely. And Third scalability and customizability. Open AI handles
Speaker 2: the massive computing infrastructure, so you don't have to worry
Speaker 2: about that scaling. Plus, you can actually fine tune the
Speaker 2: models using your own data, so you can.
Speaker 1: Train it on your specific company information or.
Speaker 2: Style precisely to suit very specific use cases, making the
Speaker 2: AI perform better for your particular needs. It becomes deeply integrated,
Speaker 2: and just like with.
Speaker 1: The techniques, there isn't just one API model is there
Speaker 1: there are different options.
Speaker 2: That's a really key point. Yeah, it's not one size
Speaker 2: fits all. There's a whole spectrum of models, mainly differing
Speaker 2: by their parameter size, which affects capability and cost.
Speaker 1: Okay, so what are we talking about.
Speaker 2: Well, at the top end, you might have something like
Speaker 2: chat GPT three point five B that's three point five
Speaker 2: billion parameters, very capable, great for complex tasks, nuanced.
Speaker 1: Conversation, probably more resource intensive.
Speaker 2: Generally, yes. Then you might step down to maybe chat
Speaker 2: GPT one point five b one point five billion parameters,
Speaker 2: still very powerful, often a good balance of performance and efficiency.
Speaker 2: Then you get smaller ones like chat GPT three hundred
Speaker 2: M three hundred million parameters, maybe better for faster responses,
Speaker 2: lower latency, or less demanding tasks right, and even smaller
Speaker 2: like chat GPT one twenty five M one hundred and
Speaker 2: twenty five million parameters design for environments where resources are
Speaker 2: really tight, maybe simple classification tasks or running on less
Speaker 2: powerful devices.
Speaker 1: So choosing the right one is a balancing act.
Speaker 2: Absolutely, you have to weigh up performance needs, how complex
Speaker 2: the task is, what computing resources, you have, latency requirements,
Speaker 2: and of course cost. It's often again an iterative process.
Speaker 2: You test model, see how it performs, get feedback, maybe
Speaker 2: try another one and refine your choice.
Speaker 1: This is all incredibly powerful stuff, but you know the
Speaker 1: old saying, with great power.
Speaker 2: Comes great responsibility.
Speaker 1: Yeah, what about the ethical side of prompt engineering? That
Speaker 1: has to be a major consideration.
Speaker 2: It absolutely is, and the book stresses us we have
Speaker 2: to think carefully about responsible and equitable.
Speaker 1: Use, like what specifically, Well.
Speaker 2: One big area is bias. The book points out how
Speaker 2: prompts with built in biases may cause discriminatory communication if
Speaker 2: we're not careful. The prompts themselves can encode assumptions or stereotypes.
Speaker 1: Right, garbage in, garbage out, potentially.
Speaker 2: Amplify exactly or issues of fairness manipulation. As we build
Speaker 2: these incredibly persuasive communication tools, we really have a duty
Speaker 2: to align them with ethical principles.
Speaker 1: So how do we do that? How do we make
Speaker 1: sure we're using prompts effectively and ethically? Is it just
Speaker 1: about having good intentions?
Speaker 2: Good intentions are a start, but it needs a process.
Speaker 2: The book really emphasizes evaluating and refining prompts as an
Speaker 2: ongoing iterative cycle, like any kind of design.
Speaker 1: Okay, so what does that cycle look like?
Speaker 2: It means first assessing the impact, asking hard questions, is
Speaker 2: this prompt actually sparking creativity like we hoped? Is it
Speaker 2: promoting critical thinking or is it accidentally leading to harmful
Speaker 2: outputs or reinforcing biases?
Speaker 1: Really checking the results against the goals yes.
Speaker 2: Then gathering feedback, actively asking the people using the prompts
Speaker 2: or interacting with the AI what's clear, what's confusing, what
Speaker 2: problems are they running into?
Speaker 1: Getting real world input.
Speaker 2: Crucial, and finally, iterative refinement. Based on that impact assessment
Speaker 2: and the feedback, You continually adjust, tweak and improve the pumps.
Speaker 2: You make sure they stay relevant, effective, and fair for
Speaker 2: different people in situations. It's not a one and done thing.
Speaker 1: It sounds like a constant conversation really, Yeah, not just
Speaker 1: the conversation with the AI, but a conversation about how
Speaker 1: we're shaping that interaction.
Speaker 2: That's a great way to put it. It's a continuous
Speaker 2: process of learning and improvement within the whole AI community.
Speaker 1: Well, we have covered a lot of ground today. We've
Speaker 1: taken this deep dive into prompt engineering right from its
Speaker 1: surprisingly long.
Speaker 2: History, Yeah, going back to Eliza all the.
Speaker 1: Way to its absolutely central role now and getting the
Speaker 1: most out of these powerful llms like chat GPT. We've
Speaker 1: seen how crafting these prompts carefully is becoming the key
Speaker 1: really to getting precise, creative, and hopefully ethically sound AI interactions,
Speaker 1: and that applies everywhere writing, business, healthcare, data science, you
Speaker 1: name it.
Speaker 2: It really is spreading across the board.
Speaker 1: And this whole prompt revolution it feels like it's just
Speaker 1: getting started, doesn't it. The book hints at things like
Speaker 1: automated prompt generation, prompts that adapt on the fly.
Speaker 2: Yeah, the future looks even more dynamic, more nuanced communication.
Speaker 1: So maybe a final thought for everyone listening today, as
Speaker 1: we get better and better at speaking the language of AI,
Speaker 1: as we learn to craft these prompts with more skill,
Speaker 1: just think what new kinds of human ingenuity might get unleashed.
Speaker 2: Hmmm, interesting question.
Speaker 1: When the tools we use can respond with this incredible
Speaker 1: precision to our thoughts are instructions, What could you create
Speaker 1: when your ideas aren't limited by clumsy interfaces anymore, but
Speaker 1: maybe only by the clarity and creativity of your own prompts.
Speaker 1: What becomes possible then,