Proving Your AI Features Work: Auditability for LLM Systems
When an auditor or enterprise buyer says 'prove your AI review process works,' teams hold one of two losing hands: nothing beyond vendor dashboards, or full prompt logs soaked in PHI. The trace schema, review records, and eval history that thread between them.
LLM audit-evidence map + content-free trace schema
Copy as markdown to paste into your repo, or download a branded PDF for sharing with non-technical stakeholders.
Frequently asked questions
How do you prove your AI features work to an auditor or enterprise buyer?
You give them provenance and process, not content. Every question an assessor asks — what data the model saw, who approved an output, what was live on a given date, whether quality is holding — is answerable from metadata, version identifiers, and linked records, so no customer payloads ever need to leave your systems.
Should you log full prompts and completions for AI observability?
Not for compliance evidence, especially when prompts are built from patient records or other sensitive data. Eighteen months of full prompts becomes an unplanned PHI repository with the wrong retention and access controls; log a content-free trace of ids, versions, hashes, and flags instead.
How can you prove what data went into a prompt without storing the prompt?
Hash each variable input with keyed HMAC-SHA-256, using a key held outside the log pipeline, and record it next to the source system's name and version. Anyone holding the key can later pull the source record, rehash it, and match — proving input provenance without keeping a second copy of the data.
How do you show change management for AI prompts and models?
Keep prompt templates in git, one file per template, changed only through reviewed PRs — the git log becomes your evidence for who changed a prompt, who approved it, and when it shipped. Pin dated model snapshot ids rather than aliases like 'latest', so you can always answer what was live on a given date.
How many eval cases do you need to prove AI quality isn't degrading?
Fifty to two hundred de-identified or synthetic cases covering your feature's real distribution is a legitimate start; a thousand-case benchmark you never run is not. Run the set weekly by cron and in CI on every prompt or model change, retain the scores, and set regression thresholds that gate promotion.
Do you need a dedicated LLM-observability tool like LangSmith or Langfuse?
Not for the compliance artifact. Those tools default to capturing full prompts and completions, which recreates the PHI-logging problem as a SaaS sub-processor you have to disclose; the content-free trace an auditor accepts is about 200 lines of middleware. They are fine for eval orchestration and content-off tracing, so build the trace first and evaluate vendors after.
Get the next one.
New operating guides as we publish them — practical, infrequent, unsubscribe in one click.
An AI feature that can't prove itself stalls the deal and the audit.
Kaan Systems does platform engineering and Terraform for funded SaaS, health-tech, and fintech. Compliance evidence is the by-product of that work. The Teardown specifies the trace, review, and eval evidence a diligence call will ask for, without a prompt archive. The Architecture and Compliance Teardown is a written read in about five business days. Book a call.