So have you ever been told by an AI that you're dead? If that happened to you, that might inspire an entire book that I'm writing and a couple series that I'm working on right now. So I'm going to talk to you about why these things suck, but why we use them in production. To do that, to test out my thesis, we're going to do something called the LAMAS. So welcome to the 2023 LAMAS, the Line Language Model Awards. In third place, beginning of the year, maybe May, there was this image that circulated a generator of AI, a stable diffusion model generated solution that spread misinformation caused the market to halt trading and run it up. Unfortunate for this lawyer decided to over-reli on the results of a chat GPT while they were filing their legal briefs. What do those two things have in common? Well, in first place would be the fact that I may not actually be here and that I'm dead. My first interaction with a language model was chat GPT in February, wrote a whole blog post in towards data science about it. Apparently, I was dead. I started a successful startup. I founded something in Ghana, but one thing that I noticed was that a lot of enterprises were interested in this thing. So the notion that they would lift and shift something that lies a lot and put it into production was frightening and still is. Large language models are trained on a large corpus of data, the world's information. So you know, garbage in and a lot of racist toxic and sexist stuff out. They're build on things that we know and so the inputs that we provide it matter, language is hard. So prompting, sometimes prompt engineering we all hear about sometimes might be like yelling at a wall or yelling at a PA system that is playing back audio. It's really hard. This happens for myriads of reasons. These are three and I'll dive into the probability reasons, the knowledge gaps and why things over fit so that Sarah Silverman in the case of overfitting doesn't come and sue you. In the case of probability, I talked about how these things are trained on a large corpus of data. And so these are books and you know stack overflow pages, that kind of stuff. Well, it's just guessing based on the last input that was provided. What's more is that the training has to be cut off at some point, right? And so after September 2021, most notably with the OpenAI, they'll let you know that it doesn't really know much after that date. But because it's trained on a lot of reasonable things, it can make some good deductive reasoning even though it doesn't have deductive reasoning. Next, if you've ever touched a machine learning model, you probably know about overfitting, which essentially means it's just regurgitating. It doesn't generalize properly and it's just regurgitating entire paragraphs. So the Sarah Silverman joke hopefully makes sense. These things are extremely powerful though. And so my clients come to me every day and say, hey, we want to rub some AI on the thing. And I tell them that a lot has to happen. First we need to let our audience know what these tools can and can't do. Notably, OpenAI Microsoft and Google will let you know that these tools will produce an accurate information. That is a good UX practice. Once you do that, there are other techniques, fine tuning being one of them. Not to solve the confabulation or hallucination problem, but to actually teach the model your unique use case tasks that matter to you. Those tame it a little bit. Other things like RAG, retrieval augmented generation, post processing and pre-processing, your user's input, chaining prompts together so that it can actually reason through what it's trying to do are very important. And we need you in the room, DevOps. I joke with David who invited me here, but what is ML ops if it's not DevOps persevering? We need some of your CI integrations to make this happen. Retrieval augmented generation is a combination of retrieval which basically lets your data be fresh and then the generation part which allows you to lean on the shoulders of the world's corpus of information that is possible through a language model. We essentially add additional complexity in order to wrestle these tools to make meaningful results for us in the enterprise. And that's possible today. In this case, I created a chatbot so that I can convince myself I'm not dead. That was the data input and the system output, retrieval augmented to generation part. But as you can see here, it's an eye chart. I'm sorry. Data input, system input, output are all parts of things that you need to use to help tame these tools. It's very difficult and very interdisciplinary in its process. Thank you.