The Stack Overflow Podcast How do you fact-check an AI?
12 snips
Apr 11, 2025 In this discussion, Amr Awadala, Co-founder and CEO of Vectara—a platform that enables the building of AI assistants—shares insights on the crucial topic of AI fact-checking. He delves into Retrieval Augmented Generation and its role in reducing AI hallucinations. Amr highlights tailored fact-checking applications for specialized fields like manufacturing and radiology, underlining the importance of source verification to prevent misinformation. The conversation also touches on challenges in ensuring AI accuracy, emphasizing the need for high-quality data management and fostering creativity through technology.
AI Snips
Chapters
Transcript
Episode notes
Grounded Hallucination Rates
- Large language models (LLMs) hallucinate less when given factual context.
- Grounded hallucination rates are decreasing, approaching a potential limit of 0.5%.
Hallucination Tolerance
- A 0.5% hallucination rate is acceptable for consumer applications but not for critical fields.
- Medicine, law, manufacturing, and government investigations demand higher accuracy.
Fact-Checking Challenges
- Open-ended fact-checking across the internet is a complex, unsolved problem.
- Vectara focuses on fact-checking within limited domain datasets.