AI / A CONCEPT NOTE
Retrieval
fetching external, real-time knowledge at query time
Overview · mechanism
pitfall · examples
01 / THE SHORT VERSION
The idea in a few sentences.
The process of pulling relevant domain documents or DB records dynamically into the prompt window before model execution, overcoming training cutoff and memory limits.
02 / FOLLOW THE MECHANISM
How retrieval pipelines execute
Query Preprocessing
expands or rephrases the incoming user question.
Retriever
queries vector DB, BM25 keyword index, or relational search.
Reranker
re-scores top-50 candidates using a cross-encoder model.
Context Formatter
truncates and formats top-K chunks into system context.
04 / COMMAND NOTES
Read the command, then the result.
Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.
query retrieval service
curl -X POST http://localhost:8000/retrieve05 / CHECK YOURSELF
Could you explain Retrieval to a teammate?
Try it out loud in two sentences: what it is, and the one detail that changes the picture. If you stall, the gap is the part to reread.
Up next in AI engineeringContext Engineeringcurating and structuring optimal prompt payloads for LLM stability