Without evaluation, every prompt change is a coin flip. RAGAS, TruLens, and custom golden sets tied to business outcomes separate serious AI products from demos that break quietly in week three. Eval is not a launch blocker you skip — it is the only way to know whether Tuesday's prompt edit helped or hurt real users.
RAGAS for retrieval quality
RAGAS scores faithfulness, answer relevance, and context precision for RAG pipelines. Run it in CI when chunking strategies, embedding models, or system prompts change. Track trends over time rather than chasing absolute thresholds on day one. Build golden sets from production failures, not only from product manager guesses — the questions users actually ask differ from the questions you wish they asked. Include adversarial cases: empty retrieval, conflicting documents, multilingual queries.
Pair automated scores with a small human review sample weekly. Metrics miss tone, cultural nuance, and domain-specific failure modes users notice immediately. Segment eval scores by tenant, language, and document type — aggregate faithfulness can hide catastrophic failures in one customer segment. Track eval runtime in CI so suites stay fast enough to run on every PR.
TruLens for trace-level feedback
TruLens instruments LangChain and LlamaIndex apps with feedback functions on traces. Product managers and domain experts can review bad answers without reading raw server logs. Define feedback functions for business-specific risks: missing citations, numeric values outside plausible range, or answers that contradict retrieved context. TruLens integrates with human labels from your product UI.
Connect thumbs-up and thumbs-down in your UI to TruLens or your own review queue from launch day. Feedback without tooling becomes anecdotal arguments in sprint planning. Export bad traces weekly to a shared review doc — product, domain experts, and engineering should see the same failure examples. Prioritize fixes by user impact score, not only by lowest automated metric.
Custom evals tied to business risk
Healthcare: citation accuracy and contraindication flags. GTM: ranking NDCG versus closed-won outcomes. Support: resolution rate without human takeover. Set tiered gates: block deploy on critical metric regression, warn on secondary metrics, document accepted tradeoffs when shipping known limitations. Healthcare and finance clients often require eval reports in security reviews.
Block deploys when critical metrics drop beyond agreed thresholds. Eval infrastructure is cheaper than reputational damage from silent regressions. Re-run eval when upstream models deprecate — provider silent upgrades have caused more production regressions than intentional prompt changes. Keep a frozen golden set versioned separately from expanding production-derived cases.
What to do next
Start with one measurable outcome — latency, accuracy, adoption, or revenue — and design your stack around that constraint. GenOrbit ships production AI systems across healthcare, astrology, fintech, and SaaS; if you want a second pair of eyes on architecture or delivery, we are happy to talk.
Related: /services/llm-rag-agent-development · /blog/prompt-engineering-production-patterns · /blog/how-to-build-rag-pipeline-langchain-fastapi