Making RAG Reliable for Enterprise Use
- Most teams don't fail at building RAG. They fail at making it reliable.
- Teams adopt auto eval, but critical decisions still heavily rely on manual review: slow, costly, and inconsistent.
- Existing evaluation and observability tools struggle to adapt as enterprise knowledge continuously evolves.
- ⚡Provide high quality evaluation while reducing up to 90% of manual evaluation, finish RAG A/B tests in minutes (not days).
- 🔍 Traces failures back to their root causes: retrieval, chunking, prompts, grounding, or outdated knowledge.
- 📈 Detects when knowledge base changes invalidate existing ground truth data.
- 🎯 One click delivers insight for both AI teams and business teams.
- 🚀Turn RAG evaluation into a fast, iterative loop
- Built from experience running production RAG systems where evaluation became the bottleneck.
- Auto Evaluation + Root Cause Analysis will become the foundation of enterprise AI.
- RAG Doctor is the first step toward:
- automated knowledge base optimization
- auto-generated ground truth datasets
- self-improving AI systems
- Iterative improvement loop
- Improve auto evaluation by add context such as query quality, data input stats, system prompt, etc.
- More precise root cause analysis
- Automatic golden dataset generation and update
- Automatic knowledge base selection
This is not just a tool, it helps shape the reliability of future AI products.
Join me if you:
- care about making AI products reliable
- want to shape an emerging standard
Ways to contribute:
- Share feedback
- Contribute to system design and development
- Contribute to core algorithms