Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Grading everything with an LLM-as-a-judge is slow, expensive and stochastic, while grading everything with regexes or exact string matches fails on natural language variation. This specialised course takes a deep dive into how to design and combine deterministic rule-based evaluators with calibrated model-graded evaluators.

Participants write Python, AST and JSON Schema validators for structural invariants, engineer critique-first pointwise and pairwise LLM judges for semantic quality, and combine both into a fast, high-precision hybrid grading pipeline.

Who Should Attend?

  • AI engineers and data scientists aiming to boost evaluator reliability while cutting judge spend
  • Software and QA engineers building automated LLM test suites
  • Technical teams writing evaluators for code generation, SQL, extraction and domain reporting
  • Prerequisite: Python programming and foundational LLM API/evaluation familiarity

Key Takeaways

  • Write fast deterministic evaluators for structure, schema, code/SQL syntax and numerical rules
  • Build lightweight semantic checks using atomic fact decomposition and NLI/embedding models
  • Engineer low-variance, critique-first model-graded judge prompts
  • Choose accurately between pointwise rubric grading and pairwise side-by-side comparison
  • Chain rule-based pre-filters with calibrated LLM judges for high-speed, high-agreement evaluation

Curriculum

The Rule-Based Evaluator Arsenal
  • Pydantic/JSON Schema, XML/Markdown structure checks, regexes, numeric tolerances and citation verification
  • AST parsing, SQL explain/dry-run validation and sandboxed execution for code generation
Semi-Deterministic and Small-Model Evaluators
  • Normalized fuzzy matching, ROUGE-L and where embedding cosine similarity succeeds or fails
  • Fast entailment and contradiction checks using cross-encoders and NLI models
Model-Graded (LLM-as-a-Judge) Prompt Architecture
  • Decomposing monolithic rubrics into single-aspect, atomic judge prompts
  • Critique-first structured output schemas that extract evidence before emitting pass: true/false
Pointwise versus Pairwise Grading and Debiasing
  • Pointwise rubrics for absolute gates versus swap-augmented pairwise comparison for subtle preferences
  • Neutralising verbosity bias, position bias and model self-preference
Hybrid Grading Chains and Human Calibration
  • Chaining Stage 1 rule checks, Stage 2 NLI/small judges and Stage 3 frontier judges
  • Tuning judge prompts against expert labels using confusion matrices (Precision, Recall, F1)

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