Evals Before Demos: Shipping Grounded RAG to Production

When engineering teams rush generative AI into production, the biggest failure mode isn't latency or token cost—it's unmeasured drift and ungrounded hallucination.

The Demo Trap A chat demo on 20 hand-picked sample documents almost always looks miraculous. But when you expose the assistant to 50,000 corporate records, subtle ambiguities, and conflicting policy revisions, ad-hoc prompt tweaks collapse.

Evals as CI Quality Gates At DosVectis, we treat retrieval evaluations the exact same way we treat regression test suites: 1. **Groundedness Scoring:** Automated checks verifying that every assertion made by the model maps directly to a citation chunk. 2. **Answer Relevance:** Measuring cosine similarity between user intent and generated synthesized answers. 3. **Faithfulness Matrix:** Rejecting responses when citation overlap drops below 92%.

# Automated CI eval step
from ragas import evaluate

scorecard = evaluate(test_dataset, metrics=[faithfulness, answer_relevancy]) assert scorecard['faithfulness'] >= 0.92, 'Eval regression: hallucination threshold failed' ```

Private-by-Default Architecture Customer data never traverses third-party training pipelines. By utilizing localized embedding stores in pgvector with strict tenant-level row isolation and automated PII masking proxies, enterprise retrieval stays auditable, secure, and compliant.