RAG and evaluation pipelines
Production-grade retrieval pipelines with automated evaluation, so quality regressions get caught before your users see them.
Timeline
4-8 weeks
Format
Hands-on build with your team
What you get
- Retrieval pipeline designed and built against your data sources
- Golden dataset and evaluation suite covering retrieval quality and answer quality
- Automated eval runs wired into CI so regressions block the merge
- Observability dashboards for retrieval hits, answer quality, and cost per query
- Documentation and runbooks so the system is owned by your team, not a vendor
Retrieval-augmented generation is easy to demo and hard to operate. The gap between a working prototype and a system you can trust is almost always the same three things: a curated golden dataset, an automated evaluation suite, and quality gates in the deployment path.
I build RAG pipelines the way good data pipelines are built: with contracts, tests, and monitoring. Retrieval quality gets measured with the same rigor as data quality, and every prompt or index change runs against the eval suite before it reaches users.
Good fit if your LLM feature works on the happy path but you cannot yet answer the question “how do we know it still works after this change?”