Few-Shot Prompting, RAG and Agents
Summary In this post, I explore my Kaggle notebook “Few‑Shot Prompting, RAG and Agents,” which demonstrates how to build a context‑aware conversational agent by combining three advanced AI techniques: few‑shot prompting to guide the LLM with in‑prompt examples, Retrieval‑Augmented Generation (RAG) to ground outputs in externally retrieved documents, and agent orchestration via LangGraph to manage a multi‑step workflow. The notebook ingests domain texts, embeds chunks with Google’s embeddings API, indexes them in a FAISS vector store, constructs dynamic prompts using LangChain’s ChatPromptTemplate and few‑shot templates, and finally wires everything together into a runnable graph that can decide when to retrieve context and when to generate answers. 1. Few‑Shot Prompting Few‑shot prompting involves providing a handful of example input–output pairs directly in the prompt to steer the model’s behavior on new queries. By conditioning the LLM with 3–7 demonstrations, you can...