Course Objective & Scope
A classic headache in LLM engineering is prompt whack-a-mole: tweaking a prompt to fix one bug silently breaks ten previously working scenarios. Provider model updates or migrating to a new quantized checkpoint can likewise introduce subtle behavioural regressions. This course teaches how to design and operate systematic regression test suites for LLM systems.
Using pytest, Promptfoo and DeepEval, participants build a regression suite combining deterministic assertions, semantic equivalence thresholds and tool-call contract checks to make prompt edits and model migrations safe.
Who Should Attend?
- Software and AI engineers building and maintaining LLM applications
- QA and test automation engineers extending test suites to GenAI systems
- Technical leads managing model upgrades and provider migrations
- Prerequisite: Python, basic pytest familiarity and foundational LLM application knowledge
Key Takeaways
- Build a regression test set covering business invariants, edge cases and past production bugs
- Write resilient assertions that validate semantically equivalent answers despite wording variance
- Verify that agents and function-calling flows invoke the expected tools with valid arguments
- Run side-by-side regression analyses during model upgrades or provider migrations
- Operate release sign-off gates that distinguish true regressions from benign output variation
Curriculum
Sources of Regression in LLM Systems
- Prompt edits, model snapshot changes, RAG chunk/top-k tweaks and tool updates
- Replacing brittle exact-string matching with a layered assertion pyramid
Deterministic and Structural Assertions
- JSON Schema, Pydantic validation, regexes, forbidden phrases and length bounds
- Checking citations, numerical consistency and language/format invariants
Semantic Equivalence and RAG/Agent Contract Tests
- Semantic checks using embedding similarity, NLI entailment and fact-level rubrics
- Asserting expected retrieved document IDs and agent tool-call trajectories
Model Migrations and Side-by-Side Comparison
- Running the baseline production model and candidate model on the same regression suite
- Win/tie/loss matrices and root-cause inspection of regressed test cases
Maintaining a Living Regression Suite
- Turning every fixed production bug into a permanent regression test case
- Curating test suite growth, tagging smoke versus full runs and release sign-off reporting
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Combining managed cloud AI platforms (Azure AI Foundry, Amazon Bedrock, Google Vertex AI) with on-premises GPU clusters in a hybrid architecture: sensitivity-based routing, portable contracts, private networking and disaster recovery.
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