Model evals tips — ISR (P4)
Practical guidance for evaluating Claude code, Claude desktop setups, Claude AI integration and crafting robust Claude prompts in Israeli deployments.
Overview
Focus on reproducible evaluation: input curation, deterministic seeds, and clear metric definitions for classification, safety and usefulness.
- Define success per use-case (assistive vs. autonomous).
- Use mixed quantitative & qualitative scoring.
- Track calibration and distributional shifts.
Methodology deep-dive
Ensure representative sampling across user intents and failure modes. Use stratified sampling and reserved held-out slices.
Use a combination of accuracy-like measures, calibration error (ECE), toxicity rates, and conversation-level success metrics.
Combine blind A/B annotation, consensus rules, and rapid triage for edge-case corrections. Document labeling guidelines clearly.
Key evaluation metrics
| Metric | Description | Target | Notes |
|---|---|---|---|
| Top-1 Accuracy | Correct primary output rate | ≥ 85% | Task-dependent |
| ECE (Calibration) | Expected calibration error | ≤ 5% | Assess per-class |
| Toxicity Rate | Proportion of unsafe outputs | ≤ 0.5% | Include adversarial prompts |
| Human Success | Rater-judged usefulness | ≥ 90% | Sample-based |
Deployment checklist
Pre-deploy
- Run adversarial prompt sweep
- Run calibration & temperature tuning
- Establish rollback criteria
Monitoring
- Alert on metric drift
- Sample failure cases weekly
- Automated safety checks
Photos & artifacts
Case study snapshot
Tel Aviv Retail Assistant — We measured end-to-end task completion within shopping dialogs. Post-tuning increased human success from 72% to 91% while keeping toxicity under 0.4%.
FAQ & notes
Use stratified slices reflecting production traffic, rare edge cases and adversarial inputs. Reserve a stable held-out set for comparability.
Automated checks provide scale; human review is essential for subjective quality, safety and prompt engineering validation.
Common mistakes: unclear metric definitions, leaky test data, and ignoring distribution shifts after deployment.
Further resources
Downloadable checklists, annotation schemas and quick evaluation scripts are available upon request. Contact us for tailored workshops and hands-on Claude prompt tuning.
