generate-test-data

Generate synthetic test data for LLM pipelines by defining dimensions of variation, creating combinatorial tuples, and using an LLM to expand them into realistic user queries. This surfaces failure cases when real data is sparse or unrepresentative.

Dimension-based tuple generation for evals is the process of defining axes of variation like query type and complexity, then producing combinatorial tuples that target anticipated LLM failures rather than relying on arbitrary random inputs.

Create realistic synthetic inputs for chatbot evaluation by drafting representative tuples with a product manager, using an LLM to convert tuples into naturalistic user queries, and filtering out low-quality prompts before execution.

Yes, you can use synthetic data to surface failure modes in retrieval systems by generating dimension-based test inputs, executing them through the full pipeline, and capturing traces for error analysis to identify unrepresentative edge cases.

You need a product manager to validate synthetic test datasets to ensure generated tuples reflect real-world scenarios. PM collaboration refines initial dimensions before LLM-driven expansion and conversion into naturalistic queries.

The best way to capture pipeline traces for LLM error analysis is to execute filtered synthetic queries through the full system. Running diverse, dimension-based inputs produces traces that reveal how the pipeline handles hard cases.