The short answer
Evals turn "the AI seems smarter" into a measurable number you can track over time. They are the difference between a charming prototype and a feature you can confidently put in front of customers or internal teams.
Why every AI project needs them from day one
LLMs are non-deterministic. Change the model, add a tool, tweak temperature or retrieval and behaviour shifts. Without a test suite you are flying blind. Regressions appear as "it used to be great and now it hallucinates on edge cases".
What a production eval suite actually looks like
- Golden dataset: real (anonymised) inputs from your domain + the output you want.
- Automated scoring: exact match, semantic similarity, LLM-as-judge with a tight rubric, or task success (did the tool calls produce the right side effect?).
- Metrics across dimensions: factual accuracy, tone/format adherence, refusal rate on bad inputs, cost per run, latency p95, escalation rate.
- Regression gates in CI or pre-deploy.
Guardrails are the runtime version
Evals prove quality before you ship. Guardrails catch problems at runtime: input filters, output validators, PII redaction, confidence thresholds, forced hand-off to humans. Both are required.
How we build and use evals at Softgen
During discovery we define the success criteria with you. We build the first 50–200 examples, wire an eval runner, and use it to validate every iteration. When we hand over, you get the harness so future changes don't silently degrade the feature. This is part of every AI build from £18,000.
If your current AI feature feels unreliable or you're about to add one, the first question is always: what's the eval plan? Send a brief and we'll map it with you.