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Prompt Templates for GPT 3.5 and other LLMs - LangChain #2

Generative AI and LLMs

In the second part of our LangChain series, we'll explore PromptTemplates, FewShotPromptTemplates, and example selectors. These are key features in LangChain that support prompt engineering for LLMs like OpenAI's GPT 3, Cohere, and Hugging Face's OS alternatives. LangChain is a popular framework that allows users to quickly build apps and pipelines around Large Language Models. It integrates directly with OpenAI's GPT-3 and GPT-3.5 models and Hugging Face's open-source alternatives like Google's flan-t5 models. It can be used for chatbots, Generative Question-Answering (GQA), Retrieval Augmented Generation (RAG), summarization, and much more. The core idea of the library is that we can "chain" together different components to create more advanced use cases around LLMs. Chains may consist of multiple components from several modules. We'll explore all of this in these videos. Part 1 (Intro): https://youtu.be/BP9fi_0XTlw Part 3 (Chains): https://youtu.be/S8j9Tk0lZHU 📌 Code notebook: https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/01-langchain-prompt-templates.ipynb 🌲 Pinecone article: https://www.pinecone.io/learn/langchain-prompt-templates/ 🎉 Subscribe for Article and Video Updates! https://jamescalam.medium.com/subscribe https://medium.com/@jamescalam/membership 👾 Discord: https://discord.gg/c5QtDB9RAP 00:00 Why prompts are important 02:42 Structure of prompts 04:10 Langchain code Setup 05:56 Langchain's PromptTemplates 08:34 Few shot learning with LLMs 13:04 Few shot prompt templates in Langchain 16:09 Length-based example selectors 21:19 Other Langchain example selectors 22:12 Final notes on prompts + Langchain

About the course

The Generative AI and Large Language Models (LLMs) course covers everything you need to know about: - Generative AI - Large Language Models (LLMs) - OpenAI, Cohere, Hugging Face - Managed vs. Open Source - LLM Libraries like LangChain and GPT Index - Long-term memory and retrieval-augmentation And more to come...