Prompt Evaluation and Optimzation

January 29, 2025

The podcast explores the intricacies of prompt engineering for large language models (LLMs), emphasizing its importance in optimizing LLM performance for specific tasks. Unlike traditional machine learning models, evaluating LLMs involves subjective metrics like context relevance, answer faithfulness, and prompt relevance.

The discussion highlights frameworks like ARES, which uses smaller LLMs as evaluators, and LLMA, which focuses on instruction-following capabilities. Techniques such as chain-of-thought prompting, few-shot prompting, and retrieval-augmented generation are presented as effective strategies to guide LLMs in reasoning, learning from examples, and leveraging external information. The podcast also underscores the role of public datasets like KILT and SuperGLUE, along with task-specific datasets such as Natural Questions, HotpotQA, and FEVER. These datasets provide standardized benchmarks to test and refine prompt engineering approaches. The hosts emphasize the need for creativity and experimentation in this evolving field, blending technical expertise with an intuitive understanding of language and machine learning.

Transcript of LM-Notebook Podcast

Speaker	Text
spk_0	Welcome back to the deep dive. Today, uh, we’re diving deep into prompt engineering for LLMs. Large language models, right? Exactly. You know, as data scientist with your machine running background, you already know how powerful these LMMs can be. Yeah, definitely. But, you know, to really get them to do what we want for specific tasks.
spk_0	We need prompt engineering. Yeah, it’s like its own art. Exactly. And that’s what we’ll unpack today, how to actually evaluate those prompts to get the absolute best performance out of LLMs for any given task. Yeah, and
spk_1	it’s
spk_1	definitely not like evaluating traditional machine learning models.
spk_0	Right. No, not at all. You know, those traditional models, you have clear metrics like accuracy or precision. With LLMs though, we’re dealing with these open-ended outputs, so evaluating them gets a little
spk_0	subjective.
spk_1	Yeah, for sure. It’s not just right or wrong anymore. You got to think about things like
spk_1	How well the LLM understands the context,
spk_0	yeah, and how faithful its response is to what we gave it.
spk_1	Exactly, like is it making things up, and then of course, how relevant that answer is to our original prompt.
spk_0	OK, so let’s break this down. Let’s say we’re working on text summarization. How would we evaluate different prompts to get good summaries from the LLM?
spk_1	Well, first we’d have to define what good even means for our summaries. Like, are we looking for a really concise overview, a detailed analysis, or something in between?
spk_0	Let’s say we want concise summaries that just get the key
spk_0	takeaways.
spk_1	All right, so we could try a few different prompts to get those right. Like we could start simple with something like summarize this text, or get a little more specific, like give me the key takeaways or even in three sentences, what are the most important
spk_1	points.
spk_0	OK, interesting. So 3 different prompts, but all trying to get the same thing. How do we know which one actually works best?
spk_1	That’s where those specific metrics for prompt evaluation come in.
spk_1	There’s been some interesting research on this. Yeah, like one system I’ve been looking at is Aries.
spk_1	Aries. Yeah, it stands for the Automated RA evaluation System. All right, RAG is retrieval augmented generation. It’s basically combining information retrieval with LLMs, super relevant to prompt engineering, because a lot of the time we want our LM to use external info to give a complete
spk_1	answer.
spk_0	OK, so AES is designed to evaluate prompts for those ar key systems. What’s different about how it evaluates things?
spk_1	So
spk_1	AES really focuses on three key metrics.
spk_1	Context, relevance, answer faithfulness, and answer relevance. Gotcha.
spk_0	So break those down for
spk_0	me.
spk_1	Sure. So context relevance is all about whether the information pulled in is actually relevant to the question. Makes sense. Answer faithfulness is about whether the generated response, the answer, is grounded in that information, not just making stuff up. And then answer relevance is about if that answer is actually relevant to the prompt, even if it is faithful to the information it was given. OK,
spk_0	so it’s looking at different angles of the LLN’s response.
spk_0	How does it actually calculate those metrics
spk_0	though?
spk_1	This is the cool part. It uses LLM judges,
spk_0	judges,
spk_1	yeah, basically smaller LLMs trained to do the evaluation. So they analyze the retrieved context, the generated answer, and the original prompt and score each of those three metrics. Wow.
spk_0	So we’re using LLMs to evaluate.
spk_0	Other LLMs
spk_1	pretty meta, right? And it’s been shown to be really effective.
spk_0	That’s wild. So walk me through how this works in practice.
spk_1	Sure. So AOS has a three stage process. First, it creates a synthetic data set of question answer pairs from a bunch of documents, so
spk_0	like a
spk_0	little practice world for the judges,
spk_1	yeah, exactly.
spk_1	Then in stage 2 it trains those LLM judges using this data set. It’s got separate judges for each of the metrics and trains them using contrastive learning.
spk_0	I’ve heard of that. Refresh my memory on contrastive learning though,
spk_1	of course. It’s basically where the model gets pairs of examples, some similar and some different, and has to learn to tell them apart. Oh
spk_0	right, yeah, yeah.
spk_1	So in this case, the judges are learning what makes a good answer based on those metrics by comparing.
spk_1	Good and bad examples.
spk_0	Interesting. So they’re learning from examples to then judge other examples, exactly.
spk_1	And then the last stage is scoring. And for that, Ariess uses a technique called prediction powered inference or PPI. PPI, catchy. Yeah, well, it uses a little bit of human anatated data to give us some confidence intervals for the
spk_0	score. So instead of one score, we get a range that tells us more about how the prompt is doing. Yep.
spk_1	And that’s a big advantage of Aries. It not only automates prompt evaluation, but also gives us a more reliable estimate of how those prompts are working.
spk_0	Aries sounds super powerful for evaluating prompts in these Ari wedgy systems, but are there other frameworks out there specifically for different LLMs or tasks?
spk_1	There are a few, yeah. One that comes to mind is LLMA. It’s focused on evaluation.
spk_1	LLMs based on how well they follow
spk_1	instructions.
spk_0	Instruction. So how will they do what the prompt actually tells them to do.
spk_1	LLM is great for checking out different prompt engineering strategies like chain of thought prompting, yeah, where you’re basically guiding the LLM to think step by step. OK,
spk_0	so it seems like choosing the right evaluation framework really depends on what kind of LOM you’re using and what you want it to do.
spk_1	Totally.
spk_1	And beyond these frameworks, there’s another really important element for evaluating and benchmarking.
spk_1	Public data sets. Oh
spk_0	right.
spk_0	Having these standardized data sets lets us compare different approaches and see what really works best overall.
spk_1	Exactly.
spk_1	And there are some amazing ones out there, like kilt and Super GLE.
spk_1	They’re incredibly diverse, covering all sorts of queries, documents, and answer types.
spk_0	So it’s like having a comprehensive testing ground for your prompt engineering skills. You got it. Kilt and Super GLE sound pretty intense. Any specific data sets within those that stand out to you? Oh
spk_1	yeah, definitely. For you, given your machine learning background and all, I think data sets like natural questions, hotpot QA, and Fever might be right up your alley.
spk_0	Interesting.
spk_0	Let’s hear about those. All
spk_1	right, so natural questions. That one’s all about question answering systems. It’s built with real Google search queries and their answers from Wikipedia.
spk_0	Sounds like a real world
spk_0	challenge.
spk_1	It is. And then there’s Hotpot QA. That one’s focused on multi-hop question answering.
spk_0	Multi-hop. So the LLM has to gather info from multiple sources to answer.
spk_1	Exactly, really puts the LLM’s reasoning and comprehension to the test.
spk_0	Makes sense. What about fever?
spk_1	Ah, fever, that stands for fact Extraction and verification. I know, right? It’s all about training LLMs to figure out if a claim is actually true. Wow. So it’s a great data set for testing both information retrieval and the LLM’s reasoning
spk_1	abilities.
spk_0	So we’ve got kilt and Super GLU as
spk_0	Big benchmarks, and then more focused ones like natural questions, Hotpot QA, and fever within
spk_0	them.
spk_1	Exactly. And the great thing about these public data sets is that they give you a standardized environment to experiment, learn, and improve your prompt engineering.
spk_0	You can
spk_0	compare your work with others and see what’s really working well in the field. Exactly. This has been a fantastic overview of how
spk_0	We can evaluate prompt engineering. We talked about the need for these specific metrics, the roles of frameworks like RREs and LLMA, and of course the importance of public data sets. And we’re just scratching the surface here. Prompt engineering is constantly evolving with new techniques and best practices. Yeah,
spk_1	it’s a really exciting time to be working in this area.
spk_0	It really is.
spk_0	But for now, we’re going to take a quick break. We’ll be right back to dive into some specific prompt engineering techniques that can really boost LLM performance. Sounds good.
spk_0	Welcome back to the deep dive. We’ve been talking about how important prompt engineering is for getting the most out of LLMs, and
spk_1	we saw that evaluating these prompts, it’s not exactly a walk in the
spk_1	park.
spk_0	Yeah, definitely not like traditional machine learning where you just look at accuracy,
spk_1	right? It’s way more nuanced because LLM output is so open ended.
spk_0	Exactly. We have to consider the context, how faithful the response is to the info we gave it.
spk_0	And how relevant the answer is to the prompt. Yeah,
spk_1	all those things matter,
spk_0	and that’s where systems like Aries come in, right, with those LLM judges trained to evaluate those aspects.
spk_1	Yeah, it’s pretty amazing how we can use LLMs to evaluate other LLMs.
spk_0	Definitely mind bending. But you mentioned before that Aries is mainly for evaluating our gag systems. What about other LLMs and tasks?
spk_1	Well, if you’re focused on how well the LLM follows instructions, there’s LLM
spk_0	bar. Oh, OK, so LLM bar would be better for evaluating prompts for things like writing creative content or translating languages.
spk_1	Exactly. It can tell us how well different prompting techniques work for following instructions and problem solving.
spk_1	Like chain of thought prompting.
spk_0	We touched on that earlier. Why is chain of thought prompting so effective?
spk_1	So with chain of thought prompting, you’re encouraging the LLM to think step by step. It basically makes the reasoning process more transparent and structured. We’re guiding the LLM to think more like a human would by breaking the problem down.
spk_0	So it’s like giving the LLM a roadmap so it doesn’t go off
spk_0	track.
spk_1	Yeah, exactly.
spk_1	And it’s been really successful for tasks that involve logic, problem solving, even common sense reasoning.
spk_0	Wow. So it’s not just about the instructions themselves, but how we structure them to help the LLM
spk_1	think. You got it. It’s about understanding the cognitive processes and mirroring them in our.
spk_0	So prompt engineering needs a deep understanding of both the task and how LLMs
spk_0	work.
spk_1	Absolutely. It’s this balance of human intuition and what the machine can do.
spk_0	Aside from chain of thought prompting, are there other techniques data scientists should know? Oh yeah,
spk_1	definitely.
spk_1	There’s fewShot prompting, which is pretty widely used.
spk_0	Remind me how that one works again. So with
spk_1	Fuhot prompting, you give the LLM a few examples of the output you want before asking it to generate its own. Oh
spk_0	right, so it’s like showing it a model answer.
spk_1	Exactly. Even just a handful of examples can really improve the quality and relevance of what it generates.
spk_0	It’s amazing that such a small amount of data can have such a big
spk_0	effect.
spk_1	It is, and the cool thing about Fuho prompting is that it’s so flexible. Yeah, you can experiment with different types and numbers of examples. You can even combine it with other techniques like chain of
spk_1	thought,
spk_0	so it’s adaptable to different tasks and LLMs
spk_1	exactly.
spk_1	And then there’s another technique we talked about briefly earlier, retrieval augmented
spk_0	prompting, right, giving the LLM extra info from external sources like a knowledge base. Yeah,
spk_1	it’s all about giving the LLM the right context to give us more comprehensive and insightful answers, like
spk_0	giving it a huge library to pull from.
spk_1	Exactly. And with retrieval augmentation, we can even personalize prompts giving users info specific to them. Oh wow,
spk_0	so like a chatbot that can access your browsing history to give you personalized recommendations.
spk_1	Exactly. It’s really pushing the boundaries of what’s possible with LLMs.
spk_0	The possibilities are pretty much endless.
spk_0	From personalized education to targeted ads, it seems like. Yeah for sure. So we’ve got chain of thought for reasoning, few shot for quick learning, and retrieval augmentation for context and personalization.
spk_0	That’s a lot of tools.
spk_1	It is.
spk_0	How does a data scientist even know where to begin with all these options? Well,
spk_1	the best approach really depends on the LLM you’re using, the task, and even the data you
spk_1	have.
spk_0	So it comes down to experimenting and figuring out what works best for each
spk_0	situation.
spk_1	Yeah, prompt engineering is all about trying things out, seeing the results, refining your prompts, and doing it all over again and
spk_0	being
spk_0	creative, right?
spk_1	Oh, absolutely. Creativity is super important in this field.
spk_0	Sounds like prompt engineering is as much an art as it is a science.
spk_1	It really is. And the better we understand how LLMs process information and generate language, the better our prompt engineering will get.
spk_0	This has
spk_0	been a fascinating look into prompt engineering. We talked about the challenges of evaluation, picking the right framework, and
spk_0	All those powerful techniques, and we’re
spk_1	only just getting started. There’s still so much to explore with
spk_0	LLMs. We’ll have to save that for another deep dive. But now let’s shift gears and talk about public data sets and their role in evaluating and benchmarking all these different approaches. Welcome back. We’ve been exploring the world of prompt engineering, talking about frameworks, techniques, all that good stuff.
spk_1	It’s a field that needs a good balance of being analytical and creative.
spk_0	Totally agree.
spk_0	We’ve covered how to choose the right evaluation framework and those prompting techniques for your task, but there’s another important piece we need to talk about.
spk_0	Public data sets for benchmarking.
spk_1	Oh yeah, those are essential. They let us compare different approaches to prompt engineering and see what works best across different tasks and
spk_1	LLMs.
spk_0	It’s like a standardized test for your prompt engineering skills. Exactly. Earlier we talked about kilt and Super GLU, those big benchmarks with a wide range of data sets. Let’s dive into some specific data sets that might be interesting for our
spk_0	listeners.
spk_1	OK, sure. We mentioned natural questions, hotpot QA and fever.
spk_1	They’re all great for evaluating prompt engineering for specific types of tasks.
spk_0	Let’s go over those again. Natural Questions focuses on question answering systems right. Yep.
spk_1	Us real Google search queries and their answers from Wikipedia. Provides a nice realistic challenge for those systems. And Hotpot
spk_0	QA is all about multi-hop question answering, where the LLM has to get info from multiple sources,
spk_1	right? It tests how well the LLM can reason, understand, and put together info from different parts of a text.
spk_0	And
spk_0	Fever focuses on fact verification. Yep.
spk_1	Fever challenges the LLM to figure out if a claim is true. It has to find info and then actually judge how accurate and reliable it is.
spk_0	So these data sets are really useful for seeing how well our prompt engineering techniques are doing in specific areas.
spk_1	Definitely.
spk_1	And it shows how important it is to choose the right data set for your task. Like if you’re building a chatbot for customer support, you’d probably want a data set that focuses on conversations and question answering.
spk_0	That makes sense.
spk_0	So for data scientists getting into prompt engineering, what’s the key takeaway about using these public data
spk_0	sets,
spk_1	experiment.
spk_1	Try different data sets, different prompting techniques, different evaluation metrics. The more you experiment, the more you learn what works and what doesn’t.
spk_0	And you might discover something totally new along the
spk_0	way.
spk_1	Exactly. This field is all about innovating and discovering new
spk_1	things.
spk_0	We’re just scratching the surface of what LLMs can do, and prompt engineering is leading the way for sure.
spk_0	This has been a great deep dive into the world of prompt engineering. We talked about evaluation frameworks, different prompting techniques, the importance of public data sets. It’s been a great overview, and it’s clear that prompt engineering isn’t just a technical skill. It’s an art that needs creativity, intuition, and a deep
spk_0	Understanding of both language and machine learning.
spk_1	Couldn’t have said it better myself.
spk_0	To our listeners, keep exploring this amazing field. Experiment, push the boundaries, see what you can do with LLMs. The future of this tech is in your hands.
spk_1	Keep
spk_0	learning,
spk_1	keep innovating, and we’ll see you next time on the Deep Dive.

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