Evaluate LLM output
Datasets, scoring, judges and regression tracking for non-deterministic systems.
Testing AI features is not testing software with a chatbot bolted on. Evaluation harnesses, RAG and agent behaviour, non-deterministic output, and the quality bars a team can actually ship against.
Part of path
Become an AI Quality Engineer
Datasets, scoring, judges and regression tracking for non-deterministic systems.
Retrieval quality, grounding, hallucination checks and failure taxonomies.
Multi-step tool use, guardrails and behaviour under adversarial input.
Thresholds, sign-off criteria and governance a product team will accept.
Models, tokens, context, temperature and where non-determinism comes from
Prompt design, embeddings, vector retrieval and grounding failures
Evaluation datasets, automated judges, human review and drift tracking
Tool-calling agents, multi-step traces, guardrails and recovery paths
Model Context Protocol, tool definitions and integration-level checks
Prompt injection, data leakage, jailbreaks and abuse testing
An evaluation harness for a real AI feature, plus mock interviews
HARI PRASAD
Practising Quality Engineer · years of experience —
| Start date | Schedule | Timezone | Seats | Reserve |
|---|---|---|---|---|
| 26-SEP-26 | Weekend batch | IST | 45 | Reserve a seat |
| 24-OCT-26 | Weekend batch | IST | 50 | Reserve a seat |
| — | Weekend batch | GST / EST | — | Reserve a seat |
Also taught as specialist modules
Take the assessment first — it will tell you whether to start here or build automation depth first.
Ask about the stack, the workload and whether your background is enough to keep pace.