Learn how DigitalOcean shipped a production docs agent on Gradient AI Platform using golden datasets, LLM-judged evals, and CI/CD-gated prompt iteration.

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DigitalOcean

DigitalOcean's engineering team shares how they built and shipped a production AI documentation assistant on their Gradient AI Platform. The post covers the full journey: architecture (embedded JS snippet + internal proxy service), evaluation methodology using golden datasets with LLM-as-a-judge metrics (correctness and ground truth adherence), and a CI/CD pipeline that gates deployments on metric thresholds. Key lessons include using Terraform for agent provisioning, building golden datasets before prompt iteration, tuning retrieval parameters (k=10 to k=5), adding keyword-to-product mappings to reduce ambiguity, and running automated evaluations on every PR. The team set release bars of 80% ground truth adherence and 95% correctness, and iterated through prompt changes, retrieval method switches (rewrite to sub-queries), and dataset cleanup to reach those numbers.

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