A support copilot that knows when evidence is not enough.
A fictional example showing how to present hybrid retrieval, answer evaluation, abstention, and human escalation as one production system.
What made this
worth solving.
The example support team had accurate knowledge spread across long, overlapping documents. A basic vector search returned plausible passages but could not reliably recognize weak evidence or conflicting policies.
Engineering choices,
not feature lists.
- 01
Combined semantic retrieval with exact-term search, then reranked candidates against the user’s intent.
- 02
Added offline evaluation sets for retrieval quality, groundedness, citation accuracy, and safe abstention.
- 03
Introduced caching, model routing, and human escalation when evidence confidence dropped below a defined threshold.
What changed after
the system shipped.
In this fictional demo, the evaluated workflow improved answer accuracy by 27% and reduced inference cost by 38% while making unsupported answers visible to reviewers.
The context behind
the result.
Fictional example. These values illustrate the content structure; they are not measured results. Replace them and attach your own supporting artifacts before publishing.
Evaluation context
- Example status
- Illustrative values only. No measured benchmark or production deployment is claimed.
- Evaluation set
- Example structure: 1,000 held-out support questions, including unsupported and conflicting-policy cases. Replace with your data source, split, count, and measurement date.
- Metric definition
- Answer accuracy = correct answers / evaluated questions. Moving from 60% to 76.2% is +16.2 percentage points, or a 27% relative improvement.
Decisions and alternatives
- Compare exact-term and semantic retrieval on the same questions before choosing a hybrid approach.
- Keep weak-evidence escalation separate from answer fluency: a plausible response is not proof of grounding.
Limits of this result
- This fictional example supplies no dataset, execution logs, or reproducible result artifact. Attach your own before making measured claims.
- Results on a fixed question set do not establish production reliability or generalization to another corpus.
| Metric | Baseline | Result |
|---|---|---|
| Correct answers / 1,000 questions (illustrative) | 600 / 1,000 · 60% | 762 / 1,000 · 76.2% |
| Normalized inference cost (illustrative) | 1.00 | 0.62 · 38% lower |