RAG Systems: Building Context-Aware AI

RAG (Retrieval-Augmented Generation) is the most important pattern in production AI. It solves the fundamental problem with LLMs: they don't know your data.

Instead of baking knowledge into model weights (fine-tuning), RAG retrieves relevant information at query time and includes it in the prompt. This gives you:

Up-to-date information: Your RAG pipeline always uses the latest data. No retraining needed.

Source attribution: Every answer can cite its sources, building user trust and enabling verification.

Cost efficiency: Updating a vector database is orders of magnitude cheaper than fine-tuning a model.

Reduced hallucinations: When the model has the right context, it's far less likely to make things up.

The 90/10 rule: RAG handles 90% of "teach the AI about my data" use cases. Fine-tuning handles the remaining 10% (style, format, specialized reasoning).

Most teams that jump to fine-tuning should have built a RAG system first. It's simpler, cheaper, and more flexible.

Your embedding model determines how well your RAG system understands user queries and matches them to relevant documents. Choose wrong and your entire pipeline underperforms.

Key evaluation criteria: - Dimension size: Higher dimensions capture more nuance but cost more to store and search (256 to 3,072 dimensions common) - Benchmark performance: MTEB scores give a standardized comparison, but always test on YOUR data - Multilingual support: Critical if your users or data span multiple languages - Speed: Embedding latency directly impacts query response time - Cost: API embedding costs vary 10x between providers

Current top picks (2026): - OpenAI text-embedding-3-large (best general-purpose) - Cohere embed-v4 (best for multilingual) - BGE-M3 (best open-source) - Voyage-3 (best for code)

The most common mistake: Using the same embedding model for all content types. Code, technical docs, and conversational text have very different semantic structures.

Always benchmark on your actual data. A model that tops MTEB might perform poorly on your specific domain.

Your vector database is the backbone of your RAG system. The right choice depends on your scale, budget, and operational complexity tolerance.

Managed services (lowest ops burden): - Pinecone: Best developer experience, scales effortlessly, but expensive at scale - Weaviate Cloud: Good hybrid search (vector + keyword), generous free tier - Qdrant Cloud: Excellent performance/price ratio, great filtering

Self-hosted (more control, more ops): - Qdrant: Rust-based, excellent performance, reasonable memory usage - Milvus: Battle-tested at scale, complex to operate - Chroma: Perfect for prototyping, not production-grade yet

The database-integrated option: pgvector with PostgreSQL. If you're already on Postgres, this eliminates an entire service from your architecture. Good enough for up to ~10M vectors.

Decision framework: - < 100K vectors: pgvector or Chroma (keep it simple) - 100K-10M vectors: Qdrant or Pinecone (managed simplicity) - > 10M vectors: Milvus or Qdrant (tuning matters)

Don't over-engineer. Start with the simplest option that meets your needs and migrate when you have real scaling problems.

Chunking strategy has more impact on RAG quality than your choice of embedding model or vector database. Most teams get this wrong.

The problem: Documents need to be split into chunks small enough for precise retrieval but large enough to retain context. Too small and you lose meaning. Too large and you dilute relevance.

Common strategies: - Fixed-size chunks (500-1000 tokens): Simple, predictable, but breaks mid-sentence/thought - Semantic chunking: Split at natural boundaries (paragraphs, sections). Better quality, more complex - Recursive chunking: Start with large chunks, recursively split if they're too big. Good balance - Document-aware chunking: Use document structure (headers, code blocks) to define boundaries

Advanced techniques: - Overlapping chunks: 10-20% overlap ensures no context is lost at boundaries - Parent-child chunking: Store small chunks for retrieval, return the parent chunk for context - Summary chunks: Create a summary chunk for each document, useful for high-level queries

The testing framework: Create 50+ test queries with known correct answers. Measure retrieval accuracy with different chunking strategies. The winner isn't always the most sophisticated approach.

Demo RAG systems work 70% of the time. Production RAG needs to work 95%+. Here's what fills that gap.

Hybrid search: Combine vector similarity with keyword matching (BM25). Vector search handles semantic understanding; keyword search catches exact terms the embeddings might miss. Use reciprocal rank fusion to combine results.

Re-ranking: After initial retrieval, use a cross-encoder model to re-rank results for relevance. This consistently improves answer quality by 10-20%.

Query transformation: Don't search with the user's raw query. Use an LLM to reformulate it, generate multiple search queries, or decompose complex questions into sub-queries.

Metadata filtering: Pre-filter by date, source, category, or access level before vector search. This prevents irrelevant results from wasting context window space.

Evaluation pipeline: Continuously measure retrieval accuracy, answer quality, and hallucination rate. Use automated evaluation (LLM-as-judge) supplemented with human review.

Feedback loops: Let users flag bad answers. Route these to your evaluation pipeline. Use them to improve chunking, retrieval, and prompt engineering.