How do you improve RAG accuracy?

Most gains come from retrieval: better chunking, stronger embeddings, hybrid semantic-plus-keyword search, and re-ranking so the right chunks reach the model. Then structure the prompt to answer only from sources, and evaluate both retrieval and answer quality.

Is RAG better than fine-tuning?

For keeping a model accurate about changing, factual, domain-specific information, RAG is usually better — it updates as your data does and is easier to audit. Fine-tuning suits teaching style, format, or narrow behaviours. Many systems use both.

RAG Implementation Guide: Building Retrieval-Augmented Generation

Q: What is retrieval-augmented generation (RAG)?

RAG retrieves relevant information from your own data and supplies it to the model as context, so answers are grounded in your sources rather than the model’s general training. It is the most reliable way to make an LLM accurate about your specific domain.

Retrieval-augmented generation is the most reliable way to make an LLM accurate about your specific data. It is also easy to implement badly. This guide covers the decisions that determine whether your RAG system is trustworthy.

8 min read·By Bryan Rodrigues

What is RAG?

Retrieval-augmented generation retrieves relevant information from your own data and supplies it to the model as context, so answers are grounded in your sources rather than the model’s general training. Instead of fine-tuning facts into a model, you fetch them at query time.

The query is embedded, relevant chunks are retrieved from a vector store, added to the prompt, and the model generates a grounded answer.

The pipeline, decision by decision

Chunking

How you split documents determines what can be retrieved. Chunks that are too large dilute relevance; too small lose meaning. Chunk along natural boundaries — sections, headings — and preserve enough context for each chunk to stand alone.

Embeddings and indexing

Chunks are embedded into vectors and stored for similarity search. The embedding model and how you index (including metadata for filtering) shape retrieval quality more than most teams expect.

Retrieval and ranking

At query time you fetch the top matches — often combining semantic and keyword search, then re-ranking. Getting the right chunks into the top few results is where accuracy is won.

Augmentation and generation

Assemble the retrieved chunks into a well-structured prompt that instructs the model to answer only from the provided sources and to say when it cannot. This is where retrieval meets context engineering.

Evaluating a RAG system

A RAG system needs two kinds of evaluation: retrieval quality (did we fetch the right chunks?) and answer quality (is the response faithful to those chunks and actually helpful?). Measure both, with a test set drawn from real questions, and you can improve the system deliberately instead of by trial and error.