Prompt Templating and Techniques in LangChain

Until 2021, to use an AI model for a specific use case, we would need to fine-tune the model weights themselves. That would require huge training data and significant computate to fine-tune any reasonably performing model. Instruction-fine-tuned large language models (LLMs) changed this fundamental rule of applying AI models to new use cases. Rather than needing to either train a model from scratch or fine-tune an existing model, these new LLMs could adapt incredibly well to a new problem or use case with nothing more than a prompt change. Prompts allow us to completely change the functionality of an AI pipeline. Through natural language, we tell our LLM what it needs to do, and with the right AI pipeline and prompting, it often works. LangChain naturally has many functionalities geared towards helping us build our prompts. We can build dynamic prompting pipelines that modify the structure and content of what we feed into our LLM based on essentially any parameter we would like. In this example, we'll explore the essentials of prompting in LangChain and apply this in a demo Retrieval Augmented Generation (RAG) pipeline. 🔗 Full Course: https://www.aurelio.ai/course/langchain 📌 Article and code: https://www.jamesbriggs.io/langchain-prompts 👋🏼 AI Consulting: https://aurelio.ai 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ #ai #coding #aiagents #langchain 00:00 Prompts are Fundamental to LLMs 02:13 Building Good LLM Prompts 07:13 LangChain Prompts Code Setup 11:36 Using our LLM with Templates 16:54 Few-shot Prompting 23:11 Chain of Thought Prompting