Use case · RAG and retrieval
Healthcare support
Healthcare assistants have to find the right guidance fast, summarize it clearly, and cite it, so every answer can be traced and trusted.
The experiment
Tune a retrieval-first RAG pipeline to balance ranking quality, stability, and context-window limits, for guideline Q&A and internal knowledge-base support.
- Dataset
- NFCorpus, a biomedical Q&A benchmark built on abstracts with human relevance judgments. A close proxy for guideline retrieval, where ranking errors change the answer.
- Agent
- A retrieval-first RAG workflow that surfaces the best supporting chunks first, so the summary stays grounded and cites its sources.
- Knobs tested
- Chunk sizeRetrieval depthRerankingContext budget
What the experiment showed
- 01Ranking quality decides answer correctness, so it was measured directly.
- 02Staying inside strict context limits avoids prompt overflows and latency spikes that break real support workflows.
How RapidFire helped
Every configuration in one run
Compared the knobs side by side on the same hardware, instead of one run after another.
Live results
Scored each version on slices of the eval set as it ran, so decisions came early.
Weak runs stopped early
Cut directions that were not working and put the compute behind the ones that were.
Winners cloned
Cloned the strongest run and changed one factor at a time to find what drove the gain.
Apply it to your data
- 1Bring clinical guidelines, care pathways, call-center articles, or coverage policies.
- 2Test chunk size and retrieval depth side by side, and measure ranking quality and failure modes.
- 3Ship the setup that gives cited summaries and predictable behavior under your context limits.