A modern AI start-up is a front-end developer plus a prompt engineer" is a popular joke on Twitter. This talk is about LangChain, a Python open-source tool for prompt engineering. You can use it with completely open-source language models or ChatGPT. I will show you how to create a prompt and get an answer from LLM. As an example application, I will show a demo of an intelligent agent using web search and generating Python code to answer questions about this conference.
There is a gold rush to apply AI to anything nowadays. Anyone can do it, you no longer need to be a Machine Learning Engineer! Just write some prompts for ChatGPT. There is a saying "During a gold rush - sell shovels". This talk is about a wonderful tool, LangChain, as easy to use as a good shovel. This talk is about LangChain, a Python open-source tool for prompt engineering. You can use it with completely open-source language models or ChatGPT. The project started 6 months ago and now has 25k Github stars and raised $10 mln. What is all this about? This talk is a gentle introduction. It will show how to: - create a simple prompt - get an answer from a Large Language Model of your choice - local or API - chain requests together to search the web, use Python REPL - make LLM choose which tools to use for complex questions - answer questions over a collection of long documents As an example application, we will code an AI agent to answer "When is the PyCon DE & PyData Berlin 2023 conference? How many days are between that date and today?" using web search and Python REPL.
Speakers: Lev Konstantinovskiy