GenAI for Java Developers 2: Techniques

Key Takeaways

When you make coffee, you've probably got choices. Different buttons you can press. Espresso, cappuccino, latte. Each button gives you a totally different drink. This is both powerful, but it can also lead to unintended consequences. I'm a cloud advocate here at Microsoft. And one thing I've learned working with developers is once you know the basics, the real fun begins when you start exploring different techniques, the different levers you can pull, and how you can get different results from those levers. Today we have Rory joining us. And what we're doing today is seeing how Gen AI offers different brewing buttons, completions, chat flows, and rag. Each one unlocks an entirely new way to use AI. Rory, I'm so excited for today's session. please take it away. >> So once you have your environment set up and you finished the getting started video and now you're ready to look at generative AI techniques and there's really three techniques we're going to look at today. So the first techniques we've got our code space running is the LLM completion app and we're going to set some break points here and we're going to go through exactly what this is doing and why this is important. So it is going to connect to your GitHub models which we already saw that you need a token for and then once it has the completion set up we're going to go into multi-turn chat and then interactive chat. So let's debug this in our code space and we'll see there exactly what that is going to do. Now for the simple completions for the chat if we go into that. So let's step into we'll see there that it's just going to say you're a concise Java expert who can explain concepts explain Java streams in one paragraph. So let's make sure that our breakpoint is in multi-hat step through that there and then we'll see that it is coming back with a simple completion basically one turn. There we go. Java streams. Now we're in multi-turn chat. So let's check and step into that. Now multi-turn chat is going to say hey you're a helpful Java tutor. What is a hashmap in Java? And then it's going to ask another question. It's going to keep the history for that. the first response and then it's going to say, "Wow, you answered that first response. How is a hashmap different from a tree map?" So, we can step through that. It's going to multi-turn conversation and then it's going to stop there. How is a hashmap different from a tree map? See, it's already asked what a hashmap. It's kept it in history. It knows that we want to know more about what the conversation is. And we're going to step through that. And it's going to say to us, um, oh, there we go. We now have, let's go and and step through that there. And we're on interactive chat there. So, it has already broken through and told us, wait a second, if you want to know about a tree map, this is the difference. And it gives us all the key differences. And it even says great question cuz we're a helpful Java tutor. The last one that we want to see is the interactive chat. Now it's already broken there. I haven't put a broke point breakpoint there. But if we go into interactive chat, it's going to say, wait, take in the question that the person is asking. So if I ask it a question here and I can go into there it's saying okay what is the question you want to go and I want to say tell me a joke why do programmers prefer dark mode because light attracts bugs and then you can go exit from that so that's the completion part of it we are now going to go into the rag or rag stands for retrieval augmented generative uh pattern and we can see here that the rag the simple reader demo is going to read in document.txt and ground itself onto some information and the information is it says GitHub models provides a convenient way to access large language models. Now over here we want them to only and not hallucinate on the information. So, we're going to read in the document. We're going to uh use the file and then we're going to say to it, don't answer any question that doesn't ground on the the document. You can see there I cannot find that information in the provided document. So, let's go in there and let's see what we're going to ask. So, we're going to ask a helpful uh a question on rags. a simple reader demo. And now let's go in and debug that. So let's go there and we're going to debug that. And it's going to go in and read that document and it's going to use the augmentation to say ask a question about the document. And I can say what is what are GitHub models? Question mark. And it's not going to ask answer anything that isn't in the document. So what are GitHub models? And it's going to go and give us our example. At the same time, it's going to use the token, the GitHub models token to go in there and to prepare the response. And it says there GitHub models um are a convenient way to access and use large language models. and it won't really ask an answer and give you the opportunity to say, "Tell me a joke." Because it's not really relevant to the underlying um instructions. And then finally, we're going to look at functions. And functions are a nice way to create small little uh procedures that help you with certain business critical functions. And we've got two functions here. Weather function example. So let's pause it there. And then calc function. Now for the weather function, we're actually going in and we're going to simulate a weather. We're going to give it the city name and it's going to return the uh temperature. You're a helpful weather assistant. Use the weather function to provide the weather and then we're going to ask it what is the weather like in Seattle. Now, this is going to need a large language model that can call functions. So, we're going to use GPT 40 mini. So let's debug that now and it should break on the weather function. So weather function example and let's step through that now and we'll see there ah we got the weather in Seattle. The same with the calculator function and the calculator function is going to perform basic calculation. We can see if we go into the calculator function example, it's just going to do a mathematical expression and very basically what is 15% of 240 and we're going to continue through that and there we go. So this is a very simple way of handling business critical information. And you can even point it out and it can go speak to external systems, but it does need the GPT 40 mini. So coming back to what we demoed today, we went through the completion app, we went through the rag, we showcased functions, and then if you look there, responsible AI, we're actually going to mention that in a later video because responsible actually is protecting all of this that we currently doing from abuse. Thank you so much, Rory. I appreciate so much the level of detail you went into into your session. But not just that, how fun and entertaining you keep it the entire time. For everybody who joined us for this episode, if you would want to visit resources related to this episode, you can find them at aka.ms/java andai for beginners. Link is in the description of this video. We'll see you in the next episode.