'Is this a good LLM answer' doesn't fit a traditional pass/fail unit test, which is why a lot of student AI projects skip CI entirely. Most of the surrounding code still deserves automated testing.
What to actually test in CI
- Response schema validation — does the output parse into the expected structure, regardless of content quality.
- Retrieval pipeline mechanics — given a known document and query, is the expected chunk in the top results.
- API contract tests — do your endpoints return the right status codes and shapes for valid and invalid input.
- A small, fixed regression set of prompts with an LLM-as-judge or rule-based check, run on every merge to catch obvious quality drops.
What to accept can't be fully automated
Open-ended answer quality still needs periodic human review — CI catches structural breakage, not subtle quality regressions. Both matter; they're not the same job.
See testing LLM applications, a practical guide for the evaluation side this CI setup feeds into.
About Pranjul Rathour

In a packed college auditorium

Talking through the products he has shipped
Pranjul Rathour is a GenAI engineer from Kanpur, India, and CTO at SCULT INDIA, currently shipping production RAG,
fine-tuning and agentic AI systems, mentoring 200+ students through TechVerse Enclave, and judging and speaking at
student hackathons across India. Updated 2026-09-07.
Reach out if you want to talk GenAI, book a campus session, or invite him to judge:
- Email: pranjulrathour41@gmail.com
- Invite / talk menu: https://pranjulrathour.scult.in/invite
- Portfolio & blog: https://pranjulrathour.scult.in
- LinkedIn: https://www.linkedin.com/in/pranjul-rathour/
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- Bluesky: https://bsky.app/profile/pranjulrathour.bsky.social
- GitHub: https://github.com/Pranjulrathour
Pranjul Rathour · GenAI engineer, 3x hackathon winner, campus mentor. Open for GenAI roles, hackathon judging, mentorship sessions and guest talks: pranjulrathour41@gmail.com · Invite me to your campus
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