Help your sales team answer customer RFPs.

Keep the first pilot inside a clear boundary

Use this pattern when a solutions or security team repeatedly answers questions from an approved knowledge library and can assign reviewers. Start with one questionnaire section and a small set of authorized users.

Resolve missing source ownership, conflicting answers, and document permissions first. If the workflow needs autonomous commitments, contract negotiation, or unattended submission, it needs a different design and additional review.

• Allowed: retrieve authorized approved material, propose a draft, attach source references, and flag uncertainty.
• Human decision: approve the exact answer version, resolve unsupported claims, and submit externally.
• Excluded from this pilot: autonomous submission, changing policies, promising certifications, or accessing documents on behalf of users who cannot read them.

Design the full path to an approved answer

1. Intake. Create a work item for each question. Record the requester and intended recipient; treat questionnaire text as untrusted input.
2. Permission check. Resolve the requester’s identity and allowed source scope before retrieval. Test revocation and cross-team denial.
3. Approved context. Retrieve current approved answers with document IDs, versions, owners, and access metadata. Keep retired or unapproved material out of the approved collection.
4. Draft generation. Produce an answer, claim-to-source references, and explicit unresolved questions. No evidence should produce an unresolved state, not a confident guess.
5. Evidence review. Check that the cited material supports the wording. A citation existing is not enough to prove the claim.
6. Human approval. Show the draft and evidence together. Bind approval to the answer version; editing it must invalidate that approval.
7. Submission and audit. An authorized person submits. Record who approved which version and when; agree retention and access rules for the review record.

Retrieved documents can contain instructions designed to redirect a model. Retrieval-augmented generation does not itself eliminate prompt injection; OWASP describes this limitation in its prompt-injection guidance. Enforce permissions and submission boundaries outside the model’s generated text.

Test the failure paths before a pilot

The following are proposed test cases, not passing results. For each, record the exact input, expected outcome, actual output, model/configuration version, reviewer, and pass/fail decision. Add representative normal questions to check usefulness as well as failure handling.

Before testing, agree which failures block release. For this proposed pilot, unauthorized disclosure, unsupported contractual claims, and bypassed approval are stop conditions. Passing this small set is not proof that all relevant failures have been covered.

Expand only after reviewing evidence

1. Offline: use synthetic or approved historical material. Review outputs and exercise denial, failure, and rollback paths.
2. Shadow: compare drafts with the existing process. Keep external submission entirely manual and measure additional review effort.
3. Limited pilot: allow a named group to use drafts, with version-bound approval and an owner available for incidents.
4. Expansion review: inspect accepted-answer quality, incidents, review time, unresolved failures, and budget. Record the accountable owner’s decision before adding users or permissions.

Write the stop procedure in advance: who disables the assistant, how connector access is revoked, where incomplete work is recovered, and how the team returns to manual drafting. Re-run the relevant tests when sources, models, prompts, or permissions change.

This lifecycle approach is informed by the NIST Generative AI Profile. This guide is a proposed operating pattern, not an assessment of compliance with that framework.

Measure the accepted work, including review

Compare the same kind of question before and during the pilot. Track first-pass accepted answers, unsupported-claim rate, reviewer minutes, end-to-end turnaround, incidents, and total operating cost. Record the sample size and exclusions alongside any result.

Cost per accepted answer = (allocated licensing + model usage + retrieval/hosting + review labor + support) ÷ accepted answers for the period. When none are accepted, report the metric as unavailable and show the costs incurred.

Keep one-time implementation and evaluation work visible separately. Avoid claiming time saved from generation latency alone; reviewers and source owners still do work.