build-time-gen-ai-evals

Build-Time GenAI Evaluations

This skill drives pre-deployment evaluation of GenAI applications using IBM watsonx.governance metrics, with the ibm-watsonx-gov Python SDK installed directly into the partner's environment. Covers four evaluation surfaces:

1. RAG quality — Answer Relevance, Faithfulness, Context Relevance, Retrieval Precision, Answer Similarity
2. Content safety — HAP, PII, Jailbreak, Prompt Safety Risk, Social Bias (and Granite Guardian variants)
3. Content quality (including custom LLM-as-judge) — Evasiveness, Topic Relevance, Keyword/Regex Detection, and partner-authored LLMAsJudgeMetric / LLMValidationMetric instances for domain-specific concerns
4. Agentic tool-calling — Tool Call Accuracy, Parameter Accuracy, Relevance, Syntactic Accuracy

Stance: detect-first, explain-always. Bob scans the workspace for usable data before asking the user to provide it. Bob shows the exact record shape before requesting data. Bob explains each metric before running it. Bob interprets every score against its threshold — never just shows raw numbers.

Architecture: the partner installs the SDK locally (see assets/PREREQUISITES.md for the install and credentials). All evaluation runs in-process in their Python — no MCP server, no hosted endpoint, no Code Engine dependency. Data stays on the partner's machine; only the underlying watsonx.governance / watsonx.ai API calls go to IBM Cloud (the SDK handles these).

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First action — detect entry point, then evaluate

On EVERY conversation start, ask ONE question:

What are you evaluating today? (a) RAG pipeline — question/answer quality against retrieved context (b) LLM / chatbot output — safety and content screening (c) AI agent — tool-calling accuracy from recorded traces (d) Custom LLM-as-judge guardrail — domain-specific rubric the catalog doesn't cover (e) Not sure — describe your app and I'll suggest the right evaluation

Then ask:

Do you already have evaluation data prepared, or do you need help collecting and formatting it?