Analyzing RAG experiment results with statistical rigor including pivot tables, effect sizes, and ANOVA. Use when comparing configurations, calculating significance, generating findings, or creating analysis reports. Triggers on "analyze results", "compare experiments", "statistical analysis", "generate findings".
Building document corpora with controlled complexity levels (clean, mixed, messy) for RAG experiments. Use when creating test datasets, applying document degradation, or organizing documents for controlled experiments. Triggers on "build corpus", "create documents", "degrade documents", "test data".
Developing Python experiment code with incremental validation, proper error handling, and project structure. Use when writing Python modules, creating experiment infrastructure, or setting up project scaffolding. Triggers on "create module", "write Python", "setup project", "implement feature".
Evaluating RAG pipelines using RAGAS metrics (context_recall, context_precision, faithfulness, answer_relevancy). Use when measuring retrieval quality, comparing RAG configurations, or running LLM-as-judge evaluation. Triggers on "evaluate", "RAGAS", "measure recall", "context precision".
Running RAG experiment configurations with checkpointing and error recovery. Use when executing batch experiments, handling experiment failures gracefully, or resuming interrupted experiment runs. Triggers on "run experiment", "execute configurations", "resume from checkpoint".