The Practical RAG Guide — From "Can Build It" to "Can Diagnose and Improve It"

The internet is full of RAG (Retrieval-Augmented Generation) tutorials. With LangChain or LlamaIndex, a chat interface over your PDFs is a half-day project. But the real value in practice starts after that point:

When retrieval quality is poor, where do you look?
How do you choose a chunk size — and how do you justify that choice?
How do you prove, with numbers, that a change actually improved things?

This series aims higher than “can implement RAG.” The goal is to explain design decisions, diagnose quality degradation, and prove improvements with numbers. Over eight parts, we pair theory with implementation and experiments.

Questions you should be able to answer by the end

By the end of the series, you should be able to answer these in your own words. In interviews and client conversations alike, this is how RAG competence gets measured.

Why use RAG? How does it differ from fine-tuning?
How do you decide chunk size?
When retrieval quality is poor, what do you look at?
When do you use vector search versus BM25? Why does hybrid search work?
Where does a reranker go? What is the recall/precision trade-off?
How do you measure hallucination?
How do you prove RAG quality improvements?
How do you keep latency and cost under control in production?

Series structure

Part	Theme	Contents
1	RAG fundamentals	Why RAG works at all. Build a minimal RAG pipeline without hiding behind frameworks
2	Embeddings and vector search	Embedding spaces, ANN, HNSW. Compare embedding models quantitatively with Recall@5
3	Chunking	Chunk size experiments (200/500/1000). Confirm the granularity-vs-context trade-off with real data
4	Serious retrieval	Dense/sparse/hybrid search, RRF, metadata filtering. Reproduce the cases where dense retrieval fails
5	Reranking	Two-stage retrieval with cross encoders. Retriever optimizes recall; reranker optimizes precision
6	RAG evaluation	Build a golden dataset and decompose quality into retrieval and generation
7	Advanced RAG	Query rewriting, HyDE, Self-RAG, Corrective RAG. Designs that don’t stop at a single search
8	Production RAG	Infrastructure as code with Terraform, observability, cost/latency monitoring. Turning a demo into a system

The final deliverable

Rather than ending with “a chat that answers questions about internal PDFs,” the series builds one complete Technical Support RAG Platform.

Manual / Design Doc / GitHub Issues
FAQ / Incident Report / API Documentation
        ↓
       RAG
        ↓
Technical Support Assistant

For a question like “Cloud Run deployment fails with a health check error. What should I investigate?”, the system returns not just an answer but:

Answer
Sources
  - runbook.md
  - incident-042.md
  - cloud-run-guide.md
Retrieval Debug
  Top1 0.91 / Top2 0.84 / Top3 0.77
Evaluation
  Faithfulness: 0.94
  Context Recall: 0.90

Build this, and your portfolio proves you understand retrieval, evaluation, improvement, and operations — not just how to call a RAG library.

How to work through it — 60 to 90 minutes a day

Each part assumes about a week. The recommended daily split:

20 min theory → 40 min implementation → 20 min experiments → 10 min explaining it in your own words

The last 10 minutes matter most. Try explaining, without looking at any notes, why making chunks too large can degrade RAG quality. The parts you can’t explain are exactly the parts you haven’t understood.

Start with Part 1: RAG fundamentals — why retrieval reduces hallucination.