batch-hypotheses

Batch-test multiple prompt variations by bundling each hypothesis with its own CONTEXT and TASK blocks into a single call. This reduces per-hypothesis overhead while producing a unified comparative synthesis across all bundled results.

Comparing LLM responses across different personas requires bundling each persona scenario as a self-contained hypothesis into a single pass. This generates a structured cross-hypothesis synthesis artifact that highlights trade-offs and identifies the winning persona frame.

Run prompt-testing experiments without high per-request overhead by bundling N small prompt variations into a single ChatGPT call. Each hypothesis must be self-contained, single-turn answerable, and capped to ensure a unified synthesis.

You can test scene framings and prompt variations simultaneously by bundling multiple short hypotheses into one request. This accelerates experimentation by applying cross-hypothesis analysis to compare different scene frames in a single pass.

Limitations of bundling prompt experiments include the requirement that each hypothesis must be self-contained and single-turn answerable. Hypotheses must also be capped, meaning multi-turn conversational flows or dependent prompt chains are not supported.

Batch-hypotheses requires no specific dependencies or components to function. You simply provide N hypotheses with CONTEXT and TASK blocks to produce a bundled, comparative response with cross-hypothesis synthesis.