RAG for Full Stack Developers: A Practical Introduction

What retrieval-augmented generation actually is, when you need it, and how to build a first version with PostgreSQL and pgvector, without a new database.

LLMs know nothing about your product docs, your customer tickets, or the report your team wrote last week. Retrieval-augmented generation (RAG) is the standard fix: instead of hoping the model knows the answer, you find the relevant text yourself and paste it into the prompt.

That is the whole idea. Everything else, embeddings, vector databases, chunking, is machinery to make "find the relevant text" work well. As a full stack developer you already have most of the skills this needs; it is closer to search than to machine learning.

The pipeline in five steps

• Split your documents into chunks (a few hundred tokens each, respecting headings and paragraphs).
• Run each chunk through an embedding model to get a vector, and store it next to the text.
• At question time, embed the user question the same way.
• Fetch the most similar chunks (cosine similarity) from your store.
• Put those chunks into the prompt and ask the model to answer using them, and to say so when the answer is not in the context.

You probably do not need a vector database

If you already run PostgreSQL, the pgvector extension adds a vector column type, similarity operators, and an index. For collections up to hundreds of thousands of chunks, it is fast enough, and it keeps your stack boring: one database, normal backups, SQL you already know.

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE chunks (
    id bigserial PRIMARY KEY,
    document_id bigint REFERENCES documents(id),
    content text NOT NULL,
    embedding vector(1536)
);

-- top 5 most similar chunks for a query embedding
SELECT content
FROM chunks
ORDER BY embedding <=> $1
LIMIT 5;

Where RAG quality is actually won

In my experience the model is rarely the weak point; retrieval is. If the right chunk is not in the context, no model can answer correctly. Three things move the needle more than anything else:

• Chunking that respects structure. Split on headings and paragraphs, not every N characters. A chunk should make sense read alone.
• Metadata filters. Combine vector similarity with plain WHERE clauses (project, language, date). Hybrid retrieval beats pure similarity on real data.
• A small evaluation set. Twenty real questions with known answers, run after every change. Without it you are tuning blind.

When you do not need RAG at all

If all the relevant text fits in the model context window comfortably, skip the pipeline and put it in the prompt. RAG earns its complexity when the corpus is large, changes often, or must be permission-filtered per user. Starting without it, and adding it when the prompt no longer fits, is a perfectly good engineering plan.