How do you build a RAG system?

You build a RAG (retrieval-augmented generation) system by connecting an LLM to your own documents so it answers from them instead of guessing. You load and split your content, turn each piece into an embedding, store those in a vector database, then at query time retrieve the most relevant pieces and feed them to the model alongside the question.

What are the steps to build a RAG pipeline?

The pipeline has two halves: an offline part that prepares your data, and a live part that runs on every question.

1. Load your documents — pull in the source content: PDFs, web pages, wiki articles, support tickets, or database records.
2. Chunk it — split each document into small passages so retrieval returns focused, relevant pieces instead of whole files.
3. Embed each chunk — run the passages through an embedding model that converts text into vectors capturing meaning.
4. Store the vectors — save them in a vector database that can search by similarity.
5. Retrieve top-k — when a question arrives, embed it too and pull back the handful of most similar chunks.
6. Augment the prompt — insert those chunks into the prompt as context, right next to the user's question.
7. Generate — the LLM writes its answer using the retrieved context, so the reply is grounded in your data rather than its training memory.

How do you make the answers more accurate?

A first working pipeline is easy; a good one takes tuning. The biggest wins usually come from a few places:

• Reranking — retrieve more chunks than you need, then use a reranker model to reorder them and keep only the best.
• Smarter chunking — sizing passages so each holds one coherent idea, with a little overlap so context isn't cut mid-sentence.
• Metadata filtering — tagging chunks by date, source, or permission so retrieval can narrow before it ranks.
• Evaluation — testing against real questions to measure whether the right chunks come back and whether the final answer is faithful to them.

What pieces do you need to run one?

Four parts do the work: an embedding model to turn text into vectors, a vector database to store and search them, an LLM to write the answer, and a thin orchestration layer that ties retrieval and generation together on each request. Plenty of open-source frameworks and hosted services cover these, so most teams assemble a RAG system rather than build every part from scratch.