Why Evals Are the New User Stories for AI
Build trust, curb hallucinations and promote your LLM apps with a rigorous, repeatable evaluation playbook
What Is an Eval, Really?
In traditional software, user stories capture the intent of a feature. For example “As a student … I want … so that …” and engineers translate that prose into code and tests. With large language models (LLMs), the prompt becomes the user story, while the uncertainty jumps ten-fold.
LLMs are stochastic. They can generate different answers to the same prompt and occasionally invent facts. Evals step in as the new safety harness. They are structured, automated checks that tell you when a model is behaving, where it drifts and whether a change in prompting, data, or model version genuinely improves outcomes. OpenAI’s open-source Evals framework is quickly becoming the de-facto toolbox for this work. github.com
Bottom line: Prompts describe intent and evals verify impact. Together they replace the crude “ship-and-hope” mindset that plagued early LLM prototypes.
Why Evals Matter More Than Ever
LLMs now live inside sales CRMs, HR portals, accounting suites, warehouse dashboards and factory floor human machine interfaces (HMIs). In each domain they face three systemic challenges:
- Hallucination risk – factual errors undermine trust.
- Data drift – new products, policies, or market conditions break carefully tuned prompts.
- Regulatory or safety exposure – bad forecasts, biased hiring recommendations or faulty maintenance advice carry real financial or human cost.
A rigorous evaluation regime tackles all three by acting as:
- Acceptance tests for new prompts or model versions.
- Regression tests that guard against silent performance decay.
- Fitness-for-purpose scorecards that business stakeholders can understand (e.g., Does the model generate a sales email that lifts open rates by ≥15%?).
Microsoft’s evaluation playbook calls this the “predefined metrics → comparison → ranking” loop. This is the same scientific method that underpins A/B testing in web analytics. learn.microsoft.com
Industry Case Studies: From PowerPoint to Production
Below are five sectors where evals have moved from theory to hard numbers on an ops dashboard.
1. Sales Automation
Use Case
An LLM generates personalised follow-up emails after a demo call and suggests next-best actions inside the CRM.
Key Metrics
| Metric | Why it matters | How to measure quickly |
| Email open rate | First proxy for relevance and subject-line quality | Mail-client tracking pixels |
| Meeting-booked rate | Direct revenue impact of copy tweaks | Calendly/CRM events |
| Rep hours saved | Operational ROI vs. manual drafting | Time-tracking plug-in |
Eval Design
Seed prompts: 50 real call summaries → “Write a follow-up email”.
Ground-truth: Sales enablement team rewrites each output to “gold” standard.
Scoring: BLEU for subject-line fluency + LLM-judge rubric for call-to-action clarity.
Threshold: ≥ 0.35 BLEU and ≥ 4/5 rubric score must hold for 95% of prompts before go-live.
Common Pitfalls & Fixes
Over-personalisation (including confidential pricing or PII) → add a “no-leak” eval with regex checks for dollar amounts or emails.
Tone drift when Marketing updates brand guidelines → include brand-style test cases in the eval suite.
2. Human Resources
Use Case
Screening inbound CVs and drafting interview questions tailored to job competencies.
Key Metrics
| Metric | Why it matters | Easy proxy |
| Time-to-shortlist | HR’s SLA to hiring managers | ATS timestamps |
| Interview-to-hire ratio | Measures shortlist quality | HRIS records |
| DEI compliance score | Bias detection | Third-party bias audit APIs |
Eval Design
Seed data: 200 anonymised CVs labelled “Strong/Weak”.
Rule-based tests: Does the model extract mandatory fields (years of experience, certifications)?
LLM-judge tests: Compare competency summaries to human recruiter notes.
Bias eval: Swap gender-coded names and rerun with acceptable variance ≤ 5%.
Challenges
Opaque weighting of education vs. experience → tie the rubric to clearly documented policy.
Regulatory scrutiny under EU AI Act → log every eval run and store artefacts for audit.
3. Finance & Accounting
Use Case
Automated reconciliation between ERP ledger entries and bank statements, plus narrative explanations for mismatches.
Key Metrics
| Metric | Why it matters | Measurement |
| Reconciliation accuracy | Financial correctness | Sample QA by controllers |
| Days to close | Speed of monthly reporting | ERP close calendar |
| False-positive rate | Avoid wasting analyst time | QA logs |
Eval Design
Deterministic tests: For “known good” ledgers, expect zero mismatches.
Generative tests: Feed synthetic edge cases (e.g., FX revaluations) and grade the LLM’s explanation quality via another model.
Materiality thresholds: Flag any miss > $500 automatically.
Alternative View
Some CFOs argue classic rule-based engines outperform LLMs for pure reconciliation. A hybrid approach, with rules for the 85% straightforward cases and LLM-generated narratives for the messy 15%, often wins on cost–benefit.
4. Inventory & Supply Chain
Use Case
Natural-language demand forecasting and stock-level recommendations for e-commerce stock keeping units (SKUs.)
Key Metrics
| Metric | Why it matters | Quick capture |
| Forecast MAPE (Mean Absolute Percentage Error) | Accuracy of demand predictions | Compare forecast vs. actual sales |
| Stockout rate | Customer experience & lost sales | WMS logs |
| Inventory turnover | Working-capital efficiency | Finance KPIs |
Eval Design
Historical back-testing: Feed last year’s data, ask LLM to forecast each week, compute MAPE.
Scenario stress tests: “Black Friday”, supply-chain delay, viral social mention.
Regeneration consistency: Same forecast prompt should yield < 2% variance across three runs. If not, tighten the temperature or add a system instruction.
Gotchas
Garbage in, garbage out: If product descriptions are inconsistent, the model mis-classifies demand drivers. Include a data-quality pre-eval that scores description completeness.
5. Manufacturing Automation
Use Case
LLM analyses sensor logs and operator notes to predict machine failures, then drafts maintenance SOPs.
Key Metrics
| Metric | Why it matters | How to measure |
| Mean Time Between Failures (MTBF) | Core reliability metric | SCADA/CMMS data |
| Overall Equipment Effectiveness (OEE) | Composite productivity | MES dashboards |
| Defect rate (PPM) | Product quality | QC inspections |
Eval Design
Classification tests: Given a labelled dataset of past failures, model must flag ≥ 92% correctly.
Procedure quality tests: Maintenance engineers rank generated standard operating procedures for clarity, safety compliance, and parts list accuracy.
Real-time drift tests: Stream live sensor data through a shadow pipeline and compare predictions to ground truth every 24 hours.
Risk & Mitigation
Hallucinated spare-part numbers could cause costly downtime. Add regex-based eval ensuring any part ID appears in the official bill of materials (BOM) database.
Building an Eval Suite: A Five-Step Checklist
- Define the business outcome. Tie each eval metric to dollars saved, risk reduced or revenue unlocked – not abstract accuracy.
- Gather high-signal test data. Curate edge cases, adversarial prompts, and real user inputs. The harder the better.
- Choose the right metric. Could be exact match (yes/no), rubric-based LLM scoring, or business-level KPIs (conversion rate). Mix as needed.
- Automate & version-control. Treat evals like code. Store them in Git, run in CI/CD and block merges if thresholds fail. The OpenAI Evals framework or LangSmith make this straightforward.
- Monitor in production. Models drift as data changes. Schedule nightly or hourly evals and trigger alerts on threshold breaches.
Pros, Cons & Pragmatic Trade-Offs
| Pros | Cons / Trade-Offs | |
| Repeatability | Removes guesswork, speeds up model iteration | Initial setup can feel bureaucratic |
| Stakeholder trust | Auditable metrics beat “just trust the AI” | Metrics can be gamed if poorly chosen |
| Risk mitigation | Catches regressions before customers do | Overly strict gates can stall innovation |
| Cross-model comparison | Swap providers (GPT-4o vs. Claude) with evidence | Requires neutral eval data to avoid selection bias |
Frequently Asked Questions
- How do I evaluate answers from ChatGPT or a no-code app I built?
Start with simple deterministic tests (exact answers) and add an LLM-judge rubric for subjective tasks like tone or creativity. - How do I check the quality of my team’s workflows?
Embed evals in every Zapier or Make scenario. If a prompt fails the gate, reroute to human review. - How do I train colleagues to cross-check responses from AI?
Teach them to look for footnotes, ask the model to cite sources, and compare against trusted internal data.
Final Thoughts
Evals are no longer a research luxury and they should be a board-level requirement. Whether you’re selling SaaS, hiring faster, closing the books, replenishing shelves, or keeping a production line humming, evals turn AI from an experiment into an asset. Embrace them early, instrument them well, and your organisation will spend less time firefighting and more time innovating.
If you’d like hands-on help designing or running evals, hit reply, explore our library of use cases, or book a discovery call. Let’s make your AI trustworthy, one eval at a time.