Build retrieval-augmented generation systems that actually work in production. From architecture decisions to evaluation frameworks, this guide covers everything you need to ship reliable RAG systems.
Retrieval-Augmented Generation (RAG) is an architecture pattern that enhances Large Language Models by providing them with relevant context from external knowledge sources. Instead of relying solely on the model's training data, RAG retrieves relevant documents at inference time and uses them to ground the model's responses.
This approach solves several fundamental LLM limitations:
However, RAG systems are only as good as their implementation. Poor chunking, inadequate retrieval, or misaligned prompts can result in systems that hallucinate just as much as vanilla LLMs—but with false confidence. This guide covers the patterns that work.
A production RAG system consists of six core components, each with its own optimization considerations. Understanding these components is essential for building systems that scale.
Load and preprocess source documents from various formats
Split documents into semantically meaningful chunks
Convert text chunks into dense vector representations
Store and index embeddings for efficient retrieval
Find relevant chunks for a given query
Generate answers using retrieved context
For production systems, separate your ingestion pipeline from your query pipeline. Ingestion can run asynchronously (batch processing, queues), while queries need low-latency synchronous execution. This separation allows independent scaling.
Chunking is often the make-or-break decision in RAG. Poor chunking leads to irrelevant retrievals and incomplete context. The right strategy depends on your document types and query patterns.
| Strategy | Best For | Trade-offs | Complexity |
|---|---|---|---|
| Fixed Size | Simple documents, consistent structure | May break semantic units | Low |
| Sentence-Based | Natural language content | Variable chunk sizes | Medium |
| Semantic | Complex documents, varied topics | Higher compute cost | High |
| Hierarchical | Long documents, multi-level retrieval | Complex implementation | High |
Embeddings convert text into numerical vectors that capture semantic meaning. Choosing the right embedding model and vector database impacts retrieval quality, latency, and cost.
| Model | Dimensions | Performance | Cost | Notes |
|---|---|---|---|---|
| OpenAI text-embedding-3-large | 3072 | Excellent | $$ | Best overall quality, supports dimension reduction |
| Cohere embed-v3 | 1024 | Very Good | $$ | Multilingual, compression options |
| Voyage AI | 1024 | Excellent | $$$ | Domain-specific models available |
| BGE-large | 1024 | Good | Free | Open source, self-hosted option |
| Mistral Embed | 1024 | Very Good | $ | European provider, GDPR-friendly |
Quick start, managed infrastructure
Hybrid search, GraphQL API
Performance, fine-grained filtering
Development, prototyping
Existing Postgres infrastructure
Basic semantic search is just the starting point. Production systems use multiple retrieval strategies to maximize relevance.
Combine dense vector search with sparse keyword search (BM25). This catches both semantic matches and exact keyword matches that vector search might miss.
Use a cross-encoder model to rerank initial retrieval results. More expensive but significantly improves relevance for top-k results.
Use an LLM to generate multiple query variations or decompose complex queries into sub-queries. Retrieve for each and merge results.
Pre-filter by metadata (date, source, category) before vector search. Essential for large document collections and multi-tenant systems.
The generation phase synthesizes retrieved context into a coherent answer. Prompt engineering and context formatting are critical for quality.
Even with 128k+ context windows, more context is not always better. Studies show that LLMs struggle with information in the "middle" of long contexts. Keep retrieved context to 3-5 highly relevant chunks, use reranking to ensure quality over quantity.
You can't improve what you don't measure. Production RAG systems need continuous evaluation across multiple dimensions.
| Metric | Description | Target | How to Measure |
|---|---|---|---|
| Retrieval Precision | % of retrieved chunks that are relevant | > 80% | Manual labeling of retrieval results |
| Retrieval Recall | % of relevant chunks that are retrieved | > 90% | Ground truth dataset comparison |
| Answer Relevance | How well the answer addresses the query | > 85% | LLM-as-judge or human evaluation |
| Faithfulness | Answer is grounded in retrieved context | > 95% | Claim extraction and verification |
| Latency (P95) | End-to-end response time | < 3s | Performance monitoring |
Open-source framework for RAG evaluation with metrics for faithfulness, relevance, and context recall.
Production observability with tracing, evaluations, and prompt versioning.
Moving from prototype to production requires addressing reliability, security, and operational concerns.
Beyond basic RAG, these patterns address specific use cases and push the boundaries of what's possible.
Use an agent loop to iteratively refine retrieval. The agent can decide when to search, what to search for, and when it has enough context to answer.
Best for complex, multi-step questionsBuild a knowledge graph from documents and traverse relationships during retrieval. Enables multi-hop reasoning and entity-centric queries.
Best for structured domains with relationshipsTrain or prompt the model to decide when retrieval is needed, assess retrieval relevance, and self-critique generated responses.
Reduces unnecessary retrievalsEvaluate retrieval quality and fall back to web search or other sources when internal knowledge is insufficient or unreliable.
Improves coverage for edge cases