Building a RAG Pipeline: Step-by-Step Tutorial

Retrieval-Augmented Generation has become the standard approach for building AI applications that need to answer questions from custom knowledge bases. Whether you are building an internal documentation assistant, a customer support bot, or a research tool, the RAG pipeline follows a consistent architecture. This tutorial walks through every step of building a production-quality RAG system, from raw documents to accurate answers.

We will cover the complete pipeline: document loading, text splitting, embedding, vector storage, retrieval, and generation. By the end, you will understand not just what each component does, but why it matters and how to optimize it.

Step 1: Document Loading and Preprocessing

Every RAG pipeline starts with your source documents. These might be PDFs, Word files, HTML pages, Markdown files, or even database records. The first challenge is converting all these formats into clean, structured text that can be processed downstream.

Document loaders handle format-specific parsing. Libraries like LangChain and LlamaIndex provide loaders for dozens of formats. For PDFs, tools like PyPDF2, pdfplumber, or Unstructured handle everything from simple text PDFs to complex layouts with tables and figures. For HTML, Beautiful Soup or similar parsers strip markup while preserving content structure.

Cleaning and Normalization

Raw extracted text often contains artifacts: headers, footers, page numbers, formatting characters, and encoding issues. A preprocessing step should normalize whitespace, remove boilerplate content, fix encoding problems, and preserve meaningful structure like headings and lists.

The quality of your RAG system is bounded by the quality of your document preprocessing. Garbage in, garbage out applies with full force to retrieval systems.

Step 2: Text Splitting (Chunking)

Language models have limited context windows, and embedding models work best with focused text segments. Chunking splits your documents into smaller pieces that can be independently embedded and retrieved. This is one of the most impactful design decisions in your pipeline.

Chunking Strategies

• Fixed-size chunking: Split text every N characters or tokens with overlap. Simple but can break mid-sentence or mid-paragraph.
• Recursive character splitting: Attempts to split on paragraph boundaries, then sentence boundaries, then word boundaries. Preserves more semantic coherence.
• Semantic chunking: Uses embedding similarity to identify natural topic boundaries within the text. More computationally expensive but produces semantically coherent chunks.
• Document-structure-aware chunking: Uses headings, sections, and other structural elements to guide splits. Ideal when documents have consistent formatting.

A good starting point is recursive splitting with chunks of 500-1000 tokens and 50-100 tokens of overlap. The overlap ensures that information near chunk boundaries is not lost.

Step 3: Generating Embeddings

Each chunk needs to be converted into a numerical vector that captures its semantic meaning. Embedding models perform this transformation, mapping text into a high-dimensional space where semantically similar texts are nearby.

Popular embedding models include text-embedding-3-small and text-embedding-3-large from OpenAI, all-MiniLM-L6-v2 from Sentence Transformers for open-source options, and Cohere's embedding models. The choice depends on your quality requirements, latency budget, and whether you need to run embeddings locally.

Embedding Best Practices

Always use the same embedding model for indexing and querying. Mixing models produces vectors in different spaces that cannot be meaningfully compared. Consider adding metadata to your chunks before embedding, such as document title or section heading, to provide additional context that improves retrieval relevance.

Step 4: Vector Storage

Embedded chunks are stored in a vector database that enables efficient similarity search. When a query arrives, its embedding is compared against all stored embeddings to find the most similar chunks.

Vector database options range from lightweight libraries to full-featured databases:

• FAISS: Facebook's open-source library for efficient similarity search. Great for prototyping and smaller datasets.
• Chroma: Developer-friendly, open-source vector database with a simple API. Good for getting started quickly.
• Pinecone: Managed vector database with built-in scaling and filtering. Ideal for production deployments.
• Weaviate: Open-source vector database with hybrid search capabilities and GraphQL API.
• Qdrant: High-performance vector database with rich filtering and payload support.

Step 5: Retrieval

When a user asks a question, the retrieval step converts the query into an embedding and finds the most similar document chunks. The basic approach is k-nearest neighbor (kNN) search, but production systems typically add several enhancements.

Metadata filtering narrows the search space based on attributes like document date, source, category, or access permissions. Hybrid search combines vector similarity with keyword matching to catch both semantic and lexical matches. Re-ranking uses a cross-encoder model to refine the initial results for higher precision.

Retrieval Parameters

The number of chunks to retrieve (k) is a critical parameter. Too few may miss relevant information; too many dilute the context with noise. Start with k=5 and adjust based on evaluation results. Consider the total token count of retrieved chunks relative to your model's context window.

Retrieval is the make-or-break step of your RAG pipeline. If the right information is not retrieved, the generator cannot produce a correct answer, no matter how capable it is.

Step 6: Prompt Construction

The prompt combines the user's question with the retrieved context to guide the language model's response. A well-designed prompt template makes a significant difference in answer quality.

An effective RAG prompt typically includes: a system instruction defining the model's role and behavior, the retrieved context chunks clearly delineated, the user's question, and instructions about how to handle cases where the context does not contain the answer. Telling the model to say "I don't have enough information to answer this question" when the context is insufficient is crucial for preventing hallucinations.

Step 7: Generation

The language model receives the constructed prompt and generates an answer. The choice of model affects quality, cost, and latency. For most RAG applications, models like GPT-4o, Claude, or Gemini provide excellent generation quality. Smaller models like GPT-4o-mini or open-source options like Llama and Mistral offer lower cost and latency for simpler use cases.

Generation Parameters

Set temperature low (0.0-0.3) for factual question-answering to minimize creativity and maximize faithfulness to the retrieved context. Use max tokens appropriate for your expected answer length. Consider enabling streaming to improve perceived latency for user-facing applications.

Production Considerations

Moving from a prototype to production requires addressing several additional concerns. Caching frequently asked questions and their responses reduces latency and cost. Monitoring tracks retrieval quality, generation latency, and user satisfaction over time. Access control ensures users only retrieve documents they are authorized to see. Error handling gracefully manages cases where retrieval fails or the model produces low-quality responses.

Build your pipeline with modularity in mind. Each component should be independently testable and replaceable. When better embedding models or vector databases become available, you should be able to swap them without rebuilding the entire system.

Start with the simplest version that works, measure its performance rigorously, and then optimize the components that have the biggest impact on your specific use case. The best RAG pipeline is not the most complex one; it is the one that reliably delivers accurate answers to your users.