How to Build Your First RAG Pipeline (Without Drowning in Jargon)

Every AI engineering job post mentions RAG. Every vendor claims to do it. And every explanation seems determined to bury a simple idea under vector-database vocabulary.

Here's the simple idea: language models don't know your documents, so you look up the relevant bits and paste them into the prompt. That's it. That's RAG — retrieval-augmented generation. Look things up, then generate.

This guide walks through building one, in plain English, in four steps.

Step 1: Chunk your documents

Models have limited context and your documents are long, so you split them into pieces — "chunks" — typically a few paragraphs each.

Chunking sounds trivial and is secretly where most RAG quality is won or lost. Two rules cover most cases:

• Respect natural boundaries. Split on sections and paragraphs, never mid-sentence. A chunk should make sense read alone.
• Overlap a little. Repeat a sentence or two between adjacent chunks so an answer that straddles a boundary isn't orphaned.

Start with chunks of roughly 300–500 words with ~50 words of overlap. Tune later, with evidence.

Step 2: Turn chunks into embeddings

An embedding is a list of numbers representing what a piece of text means. Texts with similar meaning get similar numbers. That's the entire concept — a map where "How do I reset my password?" and "I can't log into my account" land near each other despite sharing almost no words.

You get embeddings by calling an embedding model's API with each chunk. Store the resulting vectors somewhere you can search by similarity. For your first project, that does not require a dedicated vector database — a simple library or even an array you scan in a loop works fine for a few thousand chunks. Graduate to real infrastructure when scale forces you to, not before.

Step 3: Retrieve, then generate

When a question comes in:

1. Embed the question with the same model you used for the chunks.
2. Find the nearest chunks — the 3 to 10 whose vectors are most similar to the question's.
3. Build a prompt that includes those chunks and the question, with one crucial instruction: answer only from the provided context, and say "I don't know" if the context doesn't contain the answer.
4. Send it to a language model and return the response.

That instruction in step 3 is your main defense against hallucination. Without it, the model fills gaps with confident fiction. With it, a well-behaved model becomes honest about the limits of what you gave it.

Step 4: Evaluate before you celebrate

Here's where most first RAG projects go wrong: they demo well and fail quietly in production. The fix is an evaluation set — boring, unglamorous, essential.

Write 20–30 real questions with known answers from your documents. Every time you change anything — chunk size, number of retrieved chunks, the prompt — run the set and check two things:

• Retrieval hit rate: did the right chunk show up in what you retrieved? If not, no prompt will save you.
• Answer faithfulness: does the answer actually follow from the retrieved text?

Twenty questions in a spreadsheet beats vibes-based tuning every single time.

Common failure modes (so you can skip them)

• Chunks too big: retrieval gets fuzzy because each chunk is about five things at once.
• Chunks too small: the model gets fragments with no context and answers vaguely.
• Retrieving too much: stuffing 30 chunks into a prompt buries the relevant one. More context is not better context.
• Skipping the "say I don't know" instruction: this single sentence prevents the majority of embarrassing outputs.

The takeaway

RAG is four steps: chunk, embed, retrieve-and-generate, evaluate. Everything else — vector databases, rerankers, hybrid search, query rewriting — is optimization on top of this skeleton, and none of it matters until the skeleton works.

Build the simple version this weekend. Measure it. You'll understand more about how AI systems behave from one working pipeline than from a month of reading about them.