AI prompts for AI vendor evaluation: RFP questions, security review, and a scoring rubric
A practical prompt workflow for evaluating AI vendors: write a requirements brief, generate an RFP question set, run a security and privacy review, test vendors with a consistent pack, and produce an executive recommendation with traceable evidence.
Editor’s note
How to use these prompts without fooling yourself
Vendor evaluation is a place where AI hallucinations are uniquely dangerous: a confident, wrong summary can slip into a decision memo and become “truth” for weeks. The prompts below are designed to prevent that.
The rule is simple: the model may format, extract, compare, and draft—but it may not invent. You must supply the raw materials (RFP responses, security docs, architecture diagrams, DPA terms, pricing pages, demo transcripts). If a vendor does not provide evidence, the correct AI output is “Unknown” plus a follow-up question.
Step 1
Requirements brief prompt: define the use case that actually matters
Most AI evaluations fail before the first vendor call: teams start with a vendor shortlist and work backward into a justification. Flip it. Start with a one-page brief that states what you are trying to do, what data is involved, and what “success” means.
This brief becomes the anchor for your RFP, your security questions, your test pack, and your scoring rubric. It also makes internal alignment faster: leadership can approve the boundaries early.
Copy-ready prompt
Act as a procurement + security evaluation lead. Help me write a one-page AI vendor requirements brief. Context: - Organization type: [describe] - Team using it: [describe] - Primary use case: [describe] - Secondary use cases (optional): [describe] - Users and volume: [# users, # requests/day] - Data involved: [PII/PHI/PCI? internal docs? code? customer tickets?] - Deployment constraints: [SaaS only / VPC / on-prem / region] - Integrations: [SSO, CRM, ticketing, data warehouse, etc] - Compliance needs (if any): [SOC 2, HIPAA, GDPR, etc] - Risk tolerance: [low/medium/high] and why - Budget range: [range] Output: 1) Problem statement (2–4 sentences) 2) Success metrics (5–10 measurable signals) 3) Must-have requirements (10–20 bullets) 4) Nice-to-have requirements (5–15 bullets) 5) Non-goals / out of scope (5–10 bullets) 6) Evaluation plan summary (RFP + security review + POC test pack) 7) Decision owners and signoff checklist Rules: - If I omit key details, ask clarifying questions instead of guessing. - Keep language vendor-neutral and testable.
Step 2
RFP question-bank prompt: generate questions you can actually score
RFPs tend to become encyclopedias: they ask everything, which means vendors answer nothing clearly. A better approach is to generate a large question bank first, then cut it down to what you can validate or test.
Use the prompt below to generate questions grouped by category. Then prune aggressively. If you can’t verify the answer (through a test, a document, or a control), the question is mostly theater.
Copy-ready prompt
Act as an RFP author for AI vendor selection. Using the requirements brief below, draft an RFP question bank we can score. Input: [PASTE REQUIREMENTS BRIEF] Output: A) Product fit (capabilities, limitations, roadmap assumptions) B) Data handling (ingestion, retention, training usage, access controls) C) Security controls (auth, encryption, audit logs, admin controls) D) Model behavior & safety (guardrails, prompt injection defenses, evaluation) E) Reliability (SLAs, incident response, rate limits, monitoring) F) Governance (human review, approvals, policy controls) G) Integrations (SSO, APIs, connectors, webhooks, data export) H) Commercials (pricing, overages, contract terms, exit) I) Implementation (timeline, resources, customer success model) For each question include: - The question - Why we ask (1 sentence) - Evidence requested (doc/link/screenshot/log export) - Scoring guidance (0–5 with what “5” means) Rules: - Avoid generic questions. - Make each question testable. - If the brief implies regulated data, increase security/privacy depth.
Step 3
Security & privacy due diligence prompt: force evidence, not assurances
With AI vendors, security isn’t only about infrastructure. It’s about what the vendor can see (inputs, outputs, logs), what they keep (retention), what they reuse (training), and what they can do on your behalf (agentic actions).
This prompt produces a due diligence checklist and a “missing evidence” list. The goal is to surface unknowns early so you can require proof during evaluation, not after procurement momentum builds.
Copy-ready prompt
Act as a security + privacy reviewer for an AI vendor. Build a due diligence checklist and evidence request list. Inputs: - Our requirements brief: [PASTE REQUIREMENTS BRIEF] - Vendor-provided materials (paste links/excerpts): [PASTE SECURITY DOCS, TRUST CENTER LINKS, DPA TERMS, ARCHITECTURE NOTES] Output: 1) A checklist grouped by: - Data retention and deletion - Training usage of customer data (default + opt-out) - Access controls (SSO, RBAC, SCIM) - Logging and audit trails (what is logged, exportability, retention) - Encryption (in transit/at rest, key management notes) - Tenant isolation and environments - Subprocessors and third parties - Incident response (timelines, contacts, past incidents if disclosed) - Model safety controls (prompt injection, data exfiltration defenses) - Admin controls (allowed tools/actions, approvals, policy enforcement) 2) For each checklist item: - What evidence we need - Where the vendor claims it (cite exact excerpt or link) - What is missing / unclear 3) A prioritized follow-up question list (top 15) ordered by risk Rules: - Do not assume anything not supported by vendor materials. - If evidence is missing, label it “Unknown” and ask for proof. - Keep the output concise and reviewable.
Step 4
POC test-pack prompt: make vendor demos comparable
A proof-of-concept is where truth shows up. But it only works if every vendor runs the same tests against the same constraints. Your test pack should include “happy path” scenarios and failure modes (sensitive data, unsafe requests, prompt injection attempts).
The prompt below generates a POC test pack tailored to your use case, including expected outputs and a scoring guide. You still need humans to judge quality—but this gives you a consistent baseline.
Copy-ready prompt
Act as a POC test designer for selecting an AI vendor. Create a test pack that every vendor must run under the same rules. Inputs: - Requirements brief: [PASTE REQUIREMENTS BRIEF] - Our constraints: - Evaluation window: [e.g., 2 weeks] - Data allowed in testing: [synthetic only / sanitized / real] - Must run in: [SaaS / VPC / region] Output: 1) Test scenarios (10–20) grouped by: - Core task performance - Reliability and latency - Tool-use / agent behavior (if relevant) - Safety and guardrails (refusal/allow policy) - Data leakage and prompt injection resilience 2) For each scenario include: - Scenario name - Input (prompt template + any docs provided) - Constraints (what the model is allowed to do) - Expected characteristics of a good answer (rubric) - Red flags to watch for - Scoring (0–5) 3) A testing protocol: - How to run consistently - How to capture outputs - How to document failures Rules: - Do not include tests that require brand-new engineering. - Prefer tests we can run with documents, a sandbox, and controlled access. - Include at least 2 adversarial tests (prompt injection/data exfil).
Step 5
Weighted scorecard prompt: translate tradeoffs into a decision
Teams often argue about vendors because they never agreed on weighting. One group optimizes for cost, another for risk, another for output quality. A scorecard forces the discussion into explicit tradeoffs.
Use this prompt to build a weighted rubric from your requirements brief and your risk tolerance. Then review the weights with decision owners before scoring any vendor.
Copy-ready prompt
Act as an evaluation lead. Build a weighted AI vendor scorecard based on our requirements brief. Input: [PASTE REQUIREMENTS BRIEF] Output: 1) Categories with weights (total 100%), such as: - Product fit and UX - Model quality for our tasks - Security and privacy posture - Governance and admin controls - Integrations and extensibility - Reliability and support - Commercials and exit 2) For each category: - 5–12 scoring criteria - Definition of scores (0, 3, 5) with observable evidence - “Must-have” gate criteria (fail = disqualify) 3) A scoring sheet template (table) we can copy into a spreadsheet 4) A short note: what would change the weights (e.g., if we add regulated data) Rules: - Make criteria testable. - Avoid double-counting the same concept across categories. - Include an explicit exit/portability criterion (data export, logs, prompts).
Step 6
Decision memo prompt: produce an exec-ready recommendation with traceability
The last mile is where evaluations often collapse into vibes. A decision memo should be readable in ten minutes and defensible in ten months. It should show the recommendation, the tradeoffs, and the controls you will use to reduce risk after selection.
This prompt creates a memo that cites where each claim came from (RFP answer, security doc, POC result). If a claim is not supported, the memo should flag it as an assumption.
Copy-ready prompt
Act as an executive memo writer for an AI vendor selection. Draft a decision memo that is evidence-based and reviewable. Inputs: - Requirements brief: [PASTE REQUIREMENTS BRIEF] - Vendor comparison notes: - Vendor A: [paste RFP excerpts + POC results + security answers] - Vendor B: [paste] - Vendor C (optional): [paste] - Scorecard results (paste table): [PASTE SCORES] Output (max 2 pages, structured): 1) Recommendation (vendor + why now) 2) What we evaluated (scope + test pack summary) 3) Top strengths of the recommended vendor (with evidence citations) 4) Key risks and mitigations (controls we will implement) 5) Tradeoffs we accept (what we give up vs alternatives) 6) Implementation plan (first 30 days) 7) Contract terms to insist on (exit, data deletion, audit logs, support) 8) Open questions (what remains unknown) Rules: - Every major claim must reference a source from the inputs. If not supported, label as “Assumption.” - Do not invent certifications or guarantees. - Keep the tone neutral and procurement-ready.
Common pitfalls
What to watch for (and how prompts help)
AI tools can make procurement faster, but they can also make teams overconfident. These are the failure modes that show up repeatedly—and the prompt constraints that reduce them.
Mistaking “nice demo” for real fit: require a test pack and capture outputs.
Trusting vendor assurances: demand citations to vendor materials and label unknowns.
Skipping governance: score admin controls, approvals, and logging as first-class criteria.
Ignoring exit costs: include portability, export, and deletion evidence in the scorecard.
Over-optimizing for the cheapest SKU: model cost is often smaller than integration and risk cost.
Internal links
Where this fits in a practical AI operating system
Vendor evaluation is not a one-time event. The best teams treat it like a repeatable workflow: brief → evaluate → test → decide → govern. If you document your prompts and scoring, your next evaluation becomes faster and more consistent.
Related agent skill
Research Brief Agent Skill
A repeatable workflow for converting a complex topic into a clear research brief with assumptions, sources, argument map, risks, and next actions.
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FAQ
Common questions
Do these prompts replace procurement, security, or legal review?
No. They help you draft, structure, and compare, but humans still need to validate claims, review contracts, and approve risk tradeoffs. Use AI to accelerate the paperwork—not to outsource accountability.
What should I paste into the security due diligence prompt?
Paste the vendor's trust center links, security documentation excerpts, DPA terms, retention settings, architecture diagrams, and any written answers from the vendor. If you don't have evidence, the right output is “Unknown” plus follow-up questions.
How do I avoid vendor demos that don't reflect real performance?
Use a consistent POC test pack and require vendors to run the same scenarios under the same constraints. Capture outputs, compare them side-by-side, and score them with a rubric rather than impressions.
What's the biggest mistake teams make when scoring AI vendors?
They never agree on weights. A scorecard works when leadership signs off on what matters most (risk, cost, quality, speed) before the team scores any vendor.
What if vendors refuse to answer detailed security questions?
Treat refusal as risk information. Either narrow scope to non-sensitive data, require contractual terms and evidence, or choose a vendor that can support your constraints (including region, retention, and audit controls).
Final recommendation
Make the workflow repeatable before you scale it.
Use AI to accelerate the paperwork, not to outsource judgment. The best procurement prompts make vendor answers comparable, make evidence visible, and make risk tradeoffs explicit—so the final decision is reviewable by security, legal, and leadership.