Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
Data Science
Towards Data Science·20d ago·5 min read
Building Trustworthy Production RAG Systems Through Continuous Evaluation
TL;DR
RAG systems can produce fluent but incorrect answers due to retrieval failures and outdated information. A continuous evaluation pipeline is essential to identify and mitigate these issues.
✦ Why It Matters
Implement a continuous evaluation pipeline today to ensure your RAG system delivers accurate and reliable information.
Key Takeaways
- Create a golden dataset with diverse question categories.
- Perform manual checks before automating evaluation.
- Use RAGAS for efficient scoring of RAG outputs.
- Incorporate human review for nuanced assessments.
How It Works
The evaluation pipeline integrates multiple layers of assessment, starting with a golden dataset that defines correct answers. RAGAS automates scoring based on context precision and recall, while a custom LLM judge addresses domain-specific evaluation needs.
This multi-faceted approach ensures comprehensive coverage of potential failure modes.