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Building and Evaluating a Naive RAG Pipeline

Explore how to build a basic Retrieval Augmented Generation (RAG) pipeline that takes user queries and returns grounded answers from an internal document corpus. Understand the process of indexing with chunking and embeddings, performing vector search, and generating responses. Learn to evaluate retrieval precision, recall, and generation faithfulness while identifying common failure modes such as retrieval misses, context overflow, and hallucinations. Gain foundational knowledge that guides improvements in advanced RAG techniques.

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The previous lesson established RAG as a conceptual pattern built on three phases: retrieve relevant context, select the best passages, and generate a grounded answer. That pattern gives LLMs access to knowledge beyond their training data, but a concept on a whiteboard does not answer user questions. This lesson turns the pattern into running code. The goal is concrete: given a corpus of internal documents, such as a company knowledge base, build the simplest possible pipeline that accepts a user query and returns a grounded, cited answer.

This baseline implementation is called naive RAG. It uses fixed-size chunking, a single embedding model, flat cosine-similarity retrieval, and a single-shot prompt with no re-ranking or query transformation. Naive RAG is not production-ready, and that is precisely the point. Its failure modes reveal exactly where advanced techniques add value, turning each shortcoming into a clear optimization target for later lessons. The implementation uses LangChain for orchestration, ChromaDBAn open-source vector database designed for storing and querying embedding vectors with fast nearest-neighbor search. as the vector store, and OpenAI models for both embedding and generation.

Indexing: chunking, embedding, and storing

The indexing phase transforms raw documents into searchable vectors. Every retrieval decision the pipeline makes later depends on the quality of this step. Think of it like building an index for a textbook: if the index entries are poorly chosen, no amount of searching will find the right page.

Loading and chunking documents

Raw documents in formats like PDF, Markdown, or plain text must first be loaded into a uniform text representation. Once loaded, the text is split into smaller pieces called chunks. Fixed-size chunking divides the text by a character or token limit, ...