Make Your GenAI App More Relevant—Measure, Test & Improve
Most GenAI apps don’t fail because they can’t be built—they fail because they can’t be measured.
If your RAG or agentic app isn’t hitting 95%+ accuracy, it’s not ready for production. Join us live to learn how to evaluate GenAI accuracy, generate golden datasets, and improve relevance using Langflow, Astra DB, and powerful evaluation tools like RAG Checker.
You’ll walk away knowing exactly how to go from prototype to production—with less guesswork and more measurable results.
What You’ll Learn:
- How to generate a golden dataset from your own data
- Measure retrieval quality: faithfulness, hallucination, claim recall
- Evaluate and fine-tune with Langflow’s RAG toolkit
- Use tools like RAG Checker to get real-world accuracy metrics
Speakers
Adarsh Shiragannavar
Solutions Engineer
DataStax
David Jones-Gilardi
Developer Relations Engineer
DataStax
Livestream Resources
Simplifying Ground Truth Generation for LLMsGitHub Repo - Measure, Test, ImproveDataStax Developers HubSign up for Astra & Langflow
The Fastest way to Create and Share Powerful AI apps
Create powerful chatbots, agents, and RAG (retrieval augmented generation) apps in minutes, not months with a low-code, Python-based framework.