The AI Definition of Done: Establishing Quality Standards Beyond Human Review

The "Definition of Done" concept from agile development is being reexamined in the context of AI-generated work. Rather than treating human review as the quality gate, this article explores how teams should establish measurable, automated criteria for determining when AI outputs meet acceptable quality standards—a critical consideration for production local LLM deployments.

For organizations running self-hosted LLMs, establishing clear quality standards is essential before deploying agents or automation into production. This means defining metrics beyond subjective human judgment: consistency benchmarks, latency requirements, accuracy thresholds, and failure modes. For local deployments, this is particularly important because teams cannot rely on vendor support or feedback loops built into cloud APIs—they own the entire quality pipeline.

The practical implication is that successful local LLM deployments require upfront investment in defining what "done" means for your specific use case. This might include automated testing suites, reference benchmarks, continuous monitoring, and clear rollback procedures. Rather than launching with human-in-the-loop and hoping to iterate, teams should establish these standards before deployment, ensuring local inference remains reliable and trustworthy in production environments.

Source: Hacker News · Relevance: 6/10