AI Testing & Evaluations
Independent evaluation and testing for LLMs, agents and chatbots — benchmark scoring, guardrail and bias testing, and continuous regression evals.
Where we actually help.
Groundedness & hallucination testing
Measuring whether answers are actually supported by source material, not just whether they sound plausible.
Guardrail & bias testing
Adversarial and fairness testing across phrasing and demographic variations, not just a single happy-path test set.
Continuous evaluation pipelines
Re-running test sets after model, prompt or data changes, so quality regressions are caught before users find them.
Start here.
Free to read and use — no form required for the content itself.
LLM / Chatbot Evaluation Checklist
What to test before an LLM application ships.
Open →From the Blog.
Terms used in AI Testing & Evaluations.
Before you dig in.
Can LLM-as-a-judge fully replace human review?
It scales evaluation well beyond manual review alone, but has its own blind spots — most mature pipelines combine it with periodic human review, especially for safety-critical categories.
How often should evaluation run after launch?
Continuously, ideally — model providers update models and your own prompts and retrieved data drift, so a one-time pre-launch evaluation alone will miss later regressions.
Want this applied to your organization?
Every checklist here is a service we deliver — talk to our AI Testing & Evaluations practice.