Private AI pilot evaluation: evidence for a production decision

The pilot contract identifies a specific task, its users, permitted inputs, expected output and decisions that remain human. Name the business owner authorised to accept the result and the technical owner responsible for operating it. State exclusions. A draft-summary pilot does not establish that the same system can approve transactions or provide legal advice.

Record the current process: working time, observed errors, volume and review effort. This baseline lets the team test whether the proposal provides a useful improvement. Do not promise a saving percentage before measurement. The private AI business guide supports choosing a use case that matches required control and available capabilities. Resolve conflicting stakeholder expectations before collecting scores.

Include common, difficult and out-of-scope cases. For documents, cover formats, languages, lengths, missing fields and contradictory text. Add cases where the correct response is to abstain or request information. Record provenance, permission to use each input and the expected assessment. Do not copy sensitive material to an external evaluation service without reviewing that additional data flow.

Separate development data from held-out evaluation data. The first set helps improve instructions and configuration; the second checks whether improvements generalise. Repeatedly tuning against the held-out set removes that independence. With small samples, report counts and error categories and acknowledge uncertainty. Observing no failures does not demonstrate that failure is impossible, particularly for rare or deliberately adversarial inputs.

Agree thresholds with the process owner. There is no universal accuracy percentage that makes every LLM suitable for production. Use mandatory gates for non-negotiable conditions and comparative scores only for options that pass those gates.

A rubric should distinguish correct, partly useful and unacceptable responses. For summarisation, separate omissions, unsupported claims and attribution errors. For structured extraction, distinguish field accuracy from format validity: valid JSON can still contain incorrect values. Keep annotated examples so reviewers can apply the same standard, and record how borderline cases should be resolved.

Have two reviewers assess part of the sample and resolve disagreements before scoring the remainder. An automated judge can help prioritise review, but it can make mistakes or favour a particular style. Calibrate it against human assessment and do not make the tested model the sole authority on its own answers. Hugging Face evaluation considerations distinguish offline measurement from observing real use.

Pin model version, instructions, generation settings, document retrieval and serving engine. Retain case identifiers and results with restricted access. Where generation varies, repeat a subset to observe stability. Record changes between runs. Changing several components together can compare complete systems, but it makes attribution of an improvement to a single component harder.

Include negative tests: an unauthorised user, malicious instructions embedded in a document and an input exceeding limits. Check that technical controls work independently of the model remembering a prohibition. Link each result to the security plan. A failed mandatory control prevents expansion even when most ordinary outputs are useful. Record the failed test so a correction can be verified directly.

Time preparation, generation, review and correction. For draft production, remaining human effort may matter more than a few seconds of inference latency. Count accepted tasks, retries and rejected results. Compare total cost with the existing process using the same definition of completion. The cost model separates fixed expenditure from consumption and review effort.

Repeat with representative load and long inputs using the capacity test protocol. A single request from a developer laptop does not demonstrate multi-user capacity. Observe how unavailability is reported and how pending work is preserved. Users should recognise a failure rather than receive an incomplete response silently presented as a finished result.

The report should contain scope, evaluated data, configuration, results, failures and limitations. Proceed may mean a limited release to named users with human review, rather than immediate general access. Revise requires a hypothesis, owner and repeat test. Stop is a valid conclusion when no option satisfies requirements or the improvement does not justify the cost.

Before expansion, assign operations, alerts, change management and retirement responsibilities. Preserve a manual route where continuity requires one. The illustrative architecture connects these decisions without claiming customer outcomes. Private inference services can define the pilot and its evidence within an agreed scope. A final decision should identify what would trigger reassessment after release.