Scientific Research
Scientific research QA demands high-precision retrieval—missing the one critical passage can break the answer, while keeping hallucinations low and maintaining traceability back to the source paper. This example is a practical reference for optimizing those trade-offs efficiently, and a jumpstart for your own scientific or technical QA application.
This example is a practical reference for managing those trade-offs efficiently and a jumpstart for your own implementation:
How to Apply This to Your Data
This workflow shows how to operationalize Outcome Engineering for proprietary scientific corpora (papers, protocols, lab notes, internal wikis). By testing different chunking, embedding, and retrieval configurations side-by-side—and locking in winners phase-by-phase—you can engineer the retrieval behavior your QA agent needs before you optimize generation.
