Reproducible AI evaluations
How can you detect an AI regression before delivery and justify a decision to move to a pilot?
3500 € per session, excl. VAT
In short
In two days, totalling 14 hours excluding breaks, build a prototype evaluation pipeline for a generative AI application: a version-controlled corpus, explicit metrics, automated tests and a regression report. The work prepares for a supervised pilot, without claiming to deploy a complete industrial-scale infrastructure during the training.
Why this training?
A convincing answer proves neither its fidelity to source documents nor its robustness. Changes to the model, instructions or document retrieval can introduce regressions that remain invisible in a few demonstrations. An evaluation pipeline links each result to a configuration and a corpus. It combines deterministic checks, human judgement and assisted evaluations to make release trade-offs explicit. Running case study: The fictitious organisation « Clarove Maintenance » is preparing a documentation assistant for its technicians. A change to the instructions improves fluency but degrades some citations. Using fictitious manuals and questions, the team must compare two versions and decide whether a pilot is acceptable. Transfer to the workplace: Then launch a pilot within an authorised scope: appoint those responsible, adapt the corpus, run a version comparison and submit discrepancies for review before any expansion.
Objectives of this training
- Build a version-controlled evaluation corpus separate from tuning data
- Define metrics and thresholds suited to the risks
- Implement a traceable test runner
- Compare two configurations and analyse their regressions
- Formalise a decision and a pilot plan
Who is it for and what are the prerequisites?
Roles and tasks concerned
- AI engineers, developers and quality assurance managers for generative applications
- Technical product managers overseeing delivery criteria
Prior skills
- Be able to modify a script, work with JSON and use a version-controlled repository.
- Understand the instructions, parameters and limitations of a generative AI application.
- Know the principles of automated testing and continuous integration.
What you will work on
Scope the risks
Objective: Define the scope of critical behaviours. Accuracy, citations, abstention, confidentiality; severity of errors.
Assemble the corpus
Objective: Cover priority use cases. Standard, ambiguous and adversarial cases; separation of tuning and evaluation.
Annotate expected outcomes
Objective: Make judgement explicit. Acceptable answers, evidence, expected refusals and ambiguities.
Define the measurements
Objective: Translate risks into criteria. Fidelity, coverage, latency; scores by category and blocking thresholds.
Build the test runner
Objective: Automate a traceable test campaign. Configurations, identifiers, exceptions, timeouts and limited retries.
Establish the baseline
Objective: Measure initial variability. Repetitions, parameters, versions and limits of reproducibility.
Calibrate the evaluator
Objective: Establish controls for automated judgement. Position bias, justification, human–machine disagreement.
Compare the versions
Objective: Locate regressions. Paired comparison, dispersion and results by risk.
Test robustness
Objective: Test critical limits. Document-based injection, missing source, contradiction and abstention.
Simulate the release check
Objective: Apply a blocking rule. Continuous integration, artefacts, exceptions and human validation.
Prepare monitoring
Objective: Organise future evaluations. Sampling, drift, retention of traces and reannotation.
Support decision-making
Objective: Justify the move to a pilot. Evidence, reservations, rollback and stopping conditions.
What you take away
- Version-controlled corpus and annotation rubric
- Execution prototype with configurations and traces
- Comparative regression report
- Blocking policy and pilot plan
Method and assessment
- Short instructional inputs followed by immediate practice on the running case study.
- Individual work followed by work in pairs, with peer review of the outputs.
- Facilitated group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare a development environment with access only to tools authorised by the organisation. Bring an anonymised architecture; the exercises use only fictitious or anonymised cases.
Observable criteria: Each case has an identifier, a risk category and a verifiable expected outcome. ; Each run retains the versions of the corpus, instructions and declared model. ; The results distinguish between functional failure, technical error and uncertain judgement. ; Disagreements between the automated evaluator and the human evaluator are documented. ; The pilot decision applies the defined thresholds and specifies the reservations..
Frequently asked questions
Is a production solution delivered?
No: the result is a verifiable prototype accompanied by a pilot plan.
Is an AI evaluator sufficient?
No: its judgements are calibrated and supplemented with human and deterministic checks.
Is a particular provider required?
No: implementation depends on the interfaces that are available and authorised.
Can real traces be shared?
Only if anonymised and authorised; secrets and identifying data are excluded from the exercises.
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