Retrieval-Augmented Generation (RAG) has evolved from a research concept to the standard architecture for production AI applications. With 51% of enterprise AI systems now using RAG — up from 31% last year — it’s become the go-to approach for building AI that gives accurate, grounded, and up-to-date answers. This guide walks through building a RAG pipeline from scratch.
What Is RAG and Why Does It Matter?
RAG solves one of the biggest problems with large language models: hallucination. Instead of relying solely on what the model learned during training, RAG retrieves relevant documents from your own knowledge base and includes them in the prompt. The result: answers grounded in actual data rather than statistical guesses.
Think of it this way — without RAG, asking an LLM about your company’s refund policy is like asking someone who’s never worked at your company. With RAG, it’s like handing them the policy document first and then asking the question.
The Four Core Components
Every RAG pipeline consists of four stages:
- Ingestion: Loading your data — PDFs, web pages, databases, APIs — into the pipeline
- Retrieval: Finding the most relevant pieces of information for a given query
- Augmentation: Combining the retrieved context with the user’s question into a structured prompt
- Generation: Passing the augmented prompt to an LLM for a grounded response
Step 1: Document Ingestion and Chunking
Start by loading your source documents. Use libraries like LangChain for common formats (PDF, HTML, Markdown, CSV) or build custom loaders for proprietary formats.
Once loaded, split documents into chunks — typically 500-1000 tokens each with 50-100 token overlap. Chunk size is a critical parameter: too large and you waste context window space with irrelevant text; too small and you lose important surrounding context. Experiment to find the right balance for your data.
Step 2: Embedding and Indexing
Convert each chunk into a numerical vector (embedding) using a model like OpenAI’s text-embedding-3-large, Cohere’s embed-v4, or open-source alternatives like BGE or E5. Store these vectors in a vector database — popular options include Pinecone, Weaviate, Chroma, Qdrant, and pgvector for PostgreSQL.
The vector database enables semantic search: when a user asks a question, you embed their query and find the chunks whose vectors are closest in meaning — not just keyword matches, but conceptual similarity.
Step 3: Retrieval Strategies
Basic semantic search works well, but production systems benefit from advanced retrieval strategies:
- Hybrid Search: Combine semantic (vector) search with keyword (BM25) search for better recall
- Reciprocal Rank Fusion: Merge results from multiple retrieval methods into a single ranked list
- Reranking: Use a cross-encoder model to re-score retrieved chunks by relevance to the query
- Hierarchical Indexing: Create summaries of document sections and retrieve at multiple granularities
- Query Decomposition: Break complex queries into sub-queries and retrieve for each
Step 4: Prompt Assembly and Generation
Construct a prompt that combines the retrieved context with the user’s question. A typical template looks like:
“Based on the following context, answer the user’s question. If the answer isn’t in the context, say so. Context: [retrieved chunks]. Question: [user query].”
Send this to your LLM of choice — Claude, GPT-5.2, Gemini, or an open-source model via Ollama. The model generates a response grounded in your actual data rather than its training knowledge.
Agentic RAG: The 2026 Evolution
The latest evolution is Agentic RAG, where the retrieval system itself is an AI agent that can decide when to search, what to search for, and whether the results are sufficient. Using frameworks like LangGraph, you can build agents that clarify ambiguous queries, decompose complex questions into parallel sub-queries, evaluate retrieved results, and self-correct when answers are insufficient.
Key Takeaways
- Start simple — a basic RAG pipeline can be built in ~40 lines of code with LangChain
- Chunk size and retrieval strategy matter more than which LLM you use
- Hybrid search (semantic + keyword) almost always outperforms either alone
- Evaluate your pipeline with metrics like answer relevancy, faithfulness, and context precision
- Consider Agentic RAG when your queries are complex or your knowledge base is large
