A RAG Demo Is Easy. A Defensible System Is Not.

A strong project proves that trusted evidence is retrieved, used and evaluated—not simply that an LLM produced an answer.

1. Define the Domain and User Need

Specify who asks, what decisions the answers support, which sources are allowed and when the system should decline to answer.

2. Curate Trusted Source Material

Check provenance, freshness, permissions, duplicates and coverage. A retrieval system cannot ground answers in evidence it does not have.

3. Design Chunking and Metadata

Split content around meaningful ideas, preserve headings and context, and attach metadata that supports filtering and traceable citations.

4. Compare Embedding and Search Choices

Benchmark vector, keyword and hybrid retrieval on representative queries. Treat the first working configuration as a baseline, not the result.

5. Retrieve and Rerank Evidence

Tune candidate count, filters and reranking so relevant passages reach the model while plausible distractors are pushed down.

6. Ground, Cite and Refuse

Require answers to follow retrieved evidence, expose source links and abstain when support is missing or conflicting.

7. Evaluate Retrieval and Answers Separately

Measure retrieval relevance and coverage, then answer groundedness and completeness. Add latency, cost and adversarial-document tests.

Build Evidence, Not Just a Chatbot

Trusted corpus. Tested retrieval. Grounded answers. Honest evaluation. That chain turns a RAG prototype into credible engineering work.