AI evaluation frameworks
We set up AI evaluation frameworks: we systematically measure how well your AI answers — accuracy, relevance, absence of hallucinations, tone — on test sets, to improve it by data rather than 'by feel'. Honestly upfront: objectively evaluating AI quality is HARD — metrics are approximations (proxies), they do not capture everything, 'answer quality' is partly subjective; automatic evaluation can itself err, and for the important human review is needed. Eval is critically useful, but it is a measurement tool, not absolute truth.
AI evaluation frameworks — overview
AI evaluation is building a quality-assessment system for your AI application: sets of test cases (reference questions and expectations), metrics (accuracy, relevance, factuality/absence of hallucinations, tone, safety), automatic and human evaluation of answers, regression testing (whether quality dropped after prompt/model changes). It turns 'seems better' into a measurable 'better by X'. Honestly about measurement difficulty, this is key: objectively evaluating AI quality is hard. Unlike ordinary code (test passed/failed), AI has many correct answers and shades, and 'good' is partly subjective. Metrics are PROXIES (approximations), they measure individual aspects but do not capture quality as a whole. A perfect number 'AI quality = N%' does not exist. Honestly about 'the evaluator also errs': for scale LLM-as-judge (AI evaluates AI) is often used — convenient, but the evaluator itself can err and have biases. So for the important calibration and human review are needed; auto-evaluation cannot be fully trusted. Honestly about the value despite this: even imperfect eval is far better than 'improving by eye' — it catches regressions (when a prompt edit or model change quietly worsened answers), gives comparability and direction. It is the foundation of honest AI improvement. Honestly about the effect: it gives measurability and quality control but does not 'prove perfection' — it is a compass, not a guarantee. Honestly about access: examples/references and access to the AI application are needed. An important boundary: this is quality evaluation; operation/monitoring — LLMOps 898; improvement — prompts 890/RAG 893/fine-tuning 895. Picture this: instead of 'seems better' — measurable quality and protection from silent regressions. The base price starts from 45,000 ₽ (depends on the volume of tests).
Problems we solve
- AI quality is evaluated 'by eye', without measurements.
- After a prompt edit/model change quality quietly drops.
- It is unclear whether it got better or worse after changes.
- No test sets and metrics for AI.
What's included in the AI evaluation frameworks service
- Sets of test cases (references and expectations)
- Metrics (accuracy, relevance, hallucinations, tone, safety)
- Auto-evaluation (incl. LLM-as-judge) + human calibration
- Regression testing on changes
- Honest boundaries (metrics — proxies; the evaluator errs; not absolute)
- Protection from silent quality regressions
- A link with LLMOps (898) and improvement (890/893/895)
- Handover and review with you
What you get
- Measurable AI quality instead of 'by feel'
- Protection from silent regressions on changes
- Comparability and direction for improvements
- Honest boundaries (a compass, not a perfection guarantee)
How the work goes: steps
- We collect references and define metrics; clarify the critical
- We set up auto and human evaluation, regression tests
- We calibrate the evaluator, honestly set measurement boundaries with you
Why PDV Expert
- Fixed price and timeline — no surprises on the invoice.
- Report and recommendations in plain language — clear without a technical background.
- In touch at every step and answering questions about the result.
FAQ
Will eval give an exact number 'AI quality = N%'?
No, and that is honest: objectively measuring AI quality is hard. AI has many correct answers and shades, and 'good' is partly subjective. Metrics are proxies (approximations), they measure individual aspects but do not capture quality as a whole. A perfect single number does not exist. Eval gives comparability and direction but not absolute truth — we honestly state this.
If AI evaluates AI — can it be trusted?
Partly, and with calibration. LLM-as-judge (AI evaluates answers) is convenient for scale, but the evaluator itself can err and have biases. So for the important, cross-checking with human evaluation and calibration are needed. Auto-evaluation cannot be fully trusted — we honestly combine auto and human review rather than pass off the AI's evaluation as infallible.
Why eval if it is imperfect?
Because even imperfect evaluation is far better than 'improving by eye'. Eval catches regressions — when a prompt edit or model change quietly worsened answers (without it you learn of it from clients). It gives comparability and direction for improvements. It is a compass of honest AI development: not a guarantee of perfection, but protection from moving blindly.
About the provider
The «AI evaluation frameworks» service is provided by PDV Expert — a team specialising in «Site quality». We work under contract and deliver a written report with recommendations.