18 Prompts for RAG Systems: Indexing, Search, and Generation

# 18 Prompts for RAG Systems: Indexing, Search, and Generation\n\n## Introduction\n\nRetrieval-Augmented Generation (RAG) has become a cornerstone of modern AI applications, enabling language models to access and reason over external knowledge bases. However, building a production-grade RAG pipeline is far from trivial. The quality of your retrieval directly depends on three critical stages: chunking (how you split documents), embedding (how you represent text numerically), and hybrid search (combining vector and keyword retrieval). Even small mistakes in prompt design for these stages can lead to irrelevant results, hallucinations, or poor latency.\n\nThis collection of 18 ready-to-use prompts covers the entire RAG lifecycle. Each prompt is designed to be copy-pasted into your LLM interface (ChatGPT, Claude, Gemini, or open-source models) and includes a concrete use case, a code snippet, and an explanation of why it works. Whether you are a data scientist, ML engineer, or technical PM, these prompts will help you debug, optimize, and scale your RAG system without reinventing the wheel.\n\nAll prompts are based on best practices from the LangChain documentation, Pinecone’s RAG guides, and recent research (e.g., LlamaIndex, Cohere’s embedding models). No invented statistics—only proven techniques.\n\n---\n\n## 1. Prompt: Optimal Chunk Size Analyzer\n\nTask: Determine the best chunk size (in tokens) for a given document type.\n\nPrompt:\n\n```\nYou are a RAG pipeline architect. Given the following document excerpt from a technical manual, analyze the optimal chunk size for retrieval. Consider: (1) the density of entities, (2) the average sentence length, (3) the presence of tables or code blocks. Return a recommended chunk size (in tokens) and an overlap percentage. Explain your reasoning.\n\nDocument:\n\