Course Objective & Scope
An AI product's character and trustworthiness are defined by how it handles ambiguity, how it refuses out-of-policy requests, how assertively it speaks and how strictly it follows domain rules. Fixing one bad output at a time by patching prompts at random ('prompt whack-a-mole') quickly breaks other cases.
In this course participants learn how to write a clear Model Behaviour Specification, run systematic error analysis on production traces to build a failure taxonomy, and partner with domain experts to create actionable grading rubrics and an eval plan.
Who Should Attend?
- AI product managers (AI PMs) and conversation/experience designers
- AI quality assurance (QA), operations leads and domain subject-matter experts
- Applied AI engineers
- No coding prerequisites
Key Takeaways
- Author a Model Behaviour Specification with an explicit instruction hierarchy (safety > policy > accuracy > style)
- Curb false refusals, sycophancy (agreeing with mistaken user premises) and overconfident fabrications
- Conduct structured error analysis (open and axial coding) on real traces to rank failure modes
- Translate vague domain-expert critiques into binary and rubric-based evaluation criteria
- Diagnose whether a quality issue requires better prompting, cleaner RAG context, tool fixes or fine-tuning
Curriculum
Writing a Model Behaviour Specification
- Instruction hierarchy: resolving conflicts between system rules, business policy and user prompts
- Principles for tone, conciseness, epistemic humility and preventing sycophancy
Refusal Design, Edge Cases and Policy Boundaries
- Crafting helpful, non-preachy refusals and safe redirects for out-of-scope requests
- Measuring and reducing false refusals on benign edge cases
Systematic Error Analysis on Production Traces
- Sampling real traces, open coding failure patterns and consolidating a failure taxonomy
- Prioritising fixes with a frequency-by-severity matrix
Domain-Expert Rubrics and Judge Calibration
- Replacing vague 1–5 scales with unambiguous binary pass/fail criteria
- Aligning automated LLM-as-a-judge graders with human subject-matter experts
Root-Cause Diagnosis and the Quality Flywheel
- Telling apart retrieval gaps, prompt conflicts, tool schema flaws and base-model limits
- Hands-on trace review and quality improvement plan for your product
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