Course Objective & Scope
Even the most sophisticated evaluation metrics create a false sense of security when run against a shallow, repetitive or unrealistic test dataset. This course covers the end-to-end methodology for designing high-signal Golden Datasets and test cases for enterprise LLM, RAG and agent applications.
Participants construct a persona-by-task-by-difficulty coverage matrix, generate and filter grounded synthetic question-answer pairs from internal documents, craft multi-turn and adversarial edge cases, and establish an expert annotation protocol.
Who Should Attend?
- AI engineers and data scientists building LLM evaluation and quality assets
- QA engineers and technical product managers designing GenAI test scenarios
- Domain experts and analysts leading human annotation and review workflows
- Prerequisite: basic Python and foundational familiarity with LLMs and RAG
Key Takeaways
- Design a balanced test coverage matrix spanning personas, intents and difficulty levels
- Generate realistic single-hop and multi-hop synthetic test cases from company documents
- Craft edge cases covering missing context, conflicting facts, typos and prompt injections
- Write unambiguous annotation guidelines for domain experts and measure inter-annotator agreement
- Curate and prune the golden dataset over time using real production failure traces
Curriculum
Anatomy of a High-Signal Golden Dataset and Coverage Matrix
- Sizing tiers: fast smoke sets, core golden sets and full regression suites
- Building a coverage matrix across user personas, intents, complexity and input formats
Grounded Synthetic Test Data Generation and Filtering
- Generating single-hop and multi-hop questions from knowledge-base chunks
- Rewriting overly polished synthetic queries into realistic user phrasing and filtering low-quality pairs
Edge Cases, Unanswerable Queries and Adversarial Tests
- Testing abstention ('I don't know') on missing context and handling conflicting document versions
- Out-of-scope requests, PII traps, indirect prompt injections and malformed tool inputs
Domain-Expert Annotation Protocols and Quality Control
- Designing annotation rubrics and review queues that respect domain experts' time
- Defining required atomic facts and forbidden claims instead of brittle verbatim reference answers
Dataset Hygiene, Deduplication and Lifecycle Maintenance
- Pruning near-duplicate test cases using embedding similarity clustering
- Detecting and updating stale golden answers when underlying policies or documents change
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