RAG and Vector Decisions¶
Ground a model in your own data — but only after checking that a cheaper tool (grep, SQL, or just fitting it in the prompt) wouldn't have solved the problem for less.
flowchart LR
J["Junior: what an embedding is"] --> M["Middle: decide when RAG earns its cost"]
M --> S["Senior: diagnose a broken pipeline"]
S --> P["Professional: own the index lifecycle"]
Levels¶
| Level | Guide | You are done when |
|---|---|---|
| Junior | What an embedding is | You can explain cosine similarity and trace the retrieve → augment → generate loop end to end. |
| Middle | Decide when RAG earns its cost | You can use a decision framework to choose RAG, lexical search, SQL, or "just fits in the prompt" for a given case. |
| Senior | Diagnose a broken pipeline | You can identify whether a bad answer came from a recall, precision, or faithfulness failure, with evidence. |
| Professional | Own the index lifecycle | You can plan an embedding-model migration with no downtime and decide when to retire a RAG pipeline entirely. |
Practice rule¶
Before adding a vector database, write down the query you expect it to answer that grep, BM25, and SQL cannot. If you can't name one, you don't need it yet.
Related¶
- Search and Retrieval — the lexical and hybrid techniques RAG sits on top of, and the ones to try first.
- Context Fundamentals — the token budget that caps how many retrieved chunks can be used per turn.
- Agent Evaluation — the harness that measures whether a RAG change actually helped.