What is RAG (Retrieval-Augmented Generation)?

Retrieval-Augmented Generation (RAG) is an AI architecture that enhances a large language model by first retrieving relevant documents from a knowledge base, then using that retrieved context to generate a grounded, accurate response.

RAG solves one of the biggest limitations of foundation models: they only know what was in their training data. With RAG, your LLM can answer questions about documents, databases, or knowledge that was never in its training set — without retraining the model.

It's the most widely deployed pattern in production AI products today, from enterprise chatbots to internal search tools.

How RAG Works — Step by Step

A RAG pipeline has four core stages:

Ingestion — Your documents (PDFs, databases, web pages) are chunked into smaller passages and converted into vector embeddings using an embedding model.
Indexing — Those embeddings are stored in a vector database (Pinecone, Weaviate, pgvector, Chroma).
Retrieval — When a user asks a question, the query is also embedded, and the most semantically similar passages are fetched from the vector DB.
Generation — The retrieved passages are injected into the LLM's context window as a prompt, and the model generates an answer grounded in those documents.

The result: the LLM answers based on your data, not just its training knowledge.

RAG Architecture Diagram

User Query
    │
    ▼
Embedding Model
    │
    ▼
Vector Search (Top-K retrieval)
    │
    ▼
Retrieved Chunks → Context Prompt → LLM → Response

Why RAG Works So Well

RAG is effective because it separates two hard problems:

Finding relevant information — handled by the retrieval layer (fast, scalable, deterministic)
Reasoning and generation — handled by the LLM (flexible, expressive)

Neither component needs to be perfect. A good retrieval system surfaces the right context 80% of the time; the LLM fills in the gaps with reasoning. Together, they outperform either alone.

RAG vs Fine-Tuning: Which Should You Use?

This is the most common question teams face when adding custom knowledge to an LLM.

| | RAG | Fine-Tuning | |--|-----|-------------| | Use case | Dynamic, frequently updated data | Teaching new behaviors or styles | | Data freshness | Real-time — update the index anytime | Static — requires retraining | | Cost | Low — no training required | High — GPU compute + data labeling | | Setup time | Days | Weeks to months | | Hallucination risk | Lower — answers grounded in retrieved docs | Higher without careful training data | | When to use | Q&A over docs, internal knowledge bases | Custom tone, domain-specific reasoning |

Rule of thumb: If your data changes more than monthly, use RAG. If you need the model to behave differently (not just know more), consider fine-tuning — or use both together.

Common RAG Failure Modes

Teams that build naive RAG pipelines often hit these problems:

Retrieval misses — The query embedding doesn't match the document chunk, so wrong context is retrieved. Fix: better chunking strategy, hybrid search (keyword + vector).
Context too large — Stuffing 20 retrieved chunks overwhelms the model. Fix: re-rank and limit to top 3–5 relevant passages.
Chunking artifacts — Splitting documents at arbitrary character limits breaks semantic units. Fix: use sentence-aware or heading-aware chunking.
Stale embeddings — Adding new documents without re-embedding breaks recall. Fix: incremental indexing pipelines.

Production RAG Stack (2026)

A typical production RAG system uses:

Embedding model: OpenAI text-embedding-3-small, Cohere Embed, or local sentence-transformers
Vector store: pgvector (Postgres extension) for simple setups; Pinecone or Weaviate for scale
LLM: Claude 3.5 Sonnet or GPT-4o for high-quality answers
Orchestration: LangChain, LlamaIndex, or a custom retrieval loop
Re-ranking: Cohere Rerank or cross-encoder models to improve retrieval precision

When RAG Is the Right Choice

RAG is the right approach when you need:

Answers from private or proprietary documents (contracts, internal wikis, support tickets)
Up-to-date information the model wasn't trained on (product docs, recent news)
Citations and source attribution — RAG makes it easy to show which document an answer came from
Fast iteration — you can update your knowledge base without touching your model

Key Takeaway

RAG is the fastest, most cost-effective way to make an LLM useful on your own data. It's not a research concept — it's the backbone of most production AI products built in 2024–2026.

Building a RAG system for your product? Our engineers have shipped dozens of them. Book a 15-min scope call →