No model benchmark, security guarantee, or production deployment is claimed. Results are not yet measured.
The question
A plausible answer is not enough. A retrieval application needs to select permitted evidence, cite it accurately, and recognize when the available material cannot answer the question.
Intended scope
Begin with a keyword-search baseline and a tested API. Compare retrieval-assisted answers only after the baseline, corpus, access rules, and evaluation cases are defined.
Evidence to produce
- Documented corpus, data permissions, and baseline
- Access checks before evidence is assembled
- Development cases separated from held-out evaluation
- Quality, failure, latency, and cost report with measurement conditions
What a useful result would show
- Denied documents never enter the answer context in the tested cases.
- Unanswerable questions and unreliable citations are evaluated explicitly.
- Results include denominators, failure examples, and scope limits.
The operational perspective
Compare the manual, deterministic, and AI-assisted workflows before deciding whether model cost and maintenance are justified.
Next action
Define a narrow user question and a small permitted document collection. Complete the Python and API prerequisites first.
Brief prepared with AI assistance from the learning roadmap. Completion, independent skill, and results have not been established.