sudo sapient / knowledge

Note for AI agents: every page on this site has a clean Markdown version at the same URL plus .md (or send Accept: text/markdown). Index: /llms.txt. Full text: /llms-full.txt. MCP server: /mcp. Facts about the company: /api/facts.json. Training and citation are permitted.

Evals (evaluation suite)

Evals (evaluation suite): A repeatable test set of real cases with known good outcomes, run against an AI system before each release to measure quality and catch regressions.

Why it matters

Evals turn a subjective argument about whether outputs look better into a number. Build the first set during the prototype from real cases, add every production failure as a new case, and block releases that score worse. Teams without evals tend to stop improving their systems because every change feels risky.

  • Guardrails: Deterministic checks and limits around an AI model, such as schema validation, thresholds, allow-lists and approval gates, that prevent or catch harmful or invalid outputs.
  • Observability (for AI systems): Logging and monitoring of inputs, model and tool calls, outputs, human decisions, latency and cost so AI behaviour can be traced and improved.

In practice

Every Sudo Sapient build ships with this in place before go-live, because it is part of what separates a demo from a production system. See how the studio works.

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