AI Explained: Metrics to Detect Hallucinations
April 25, 2024
10AM PT / 1PM ET
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Deploying LLM applications for real-world use cases requires a comprehensive workflow to ensure LLM applications generate high-quality and accurate content. Testing, fixing issues, and measuring impact are critical steps of the workflow to help LLM applications deliver value.
Pradeep Javangula, Chief AI Officer at RagaAI will discuss strategies and practical approaches organizations can follow to maintain high performing, correct, and safe LLM applications.
Watch this AI Explained to learn:
- Methodologies in choosing the right metrics to measure the health and performance of LLM applications
- Practical approaches to evaluating and monitoring hallucination metrics, and resolving issues
- Best practices for testing and the development of LLM applications
AI Explained is our AMA series featuring experts on the most pressing issues facing AI and ML teams.
Featured Speakers
Pradeep Javangula
Chief AI Officer
at
RagaAI
With over 25 years of expertise in AI and ML, Pradeep Javangula is a renowned industry expert in AI. Previous roles include VP of AI/ML at Workday and VP of Engineering at Adobe.
Joshua Rubin
Head of AI Science
at
Fiddler AI
Joshua Rubin is Head of AI Science at Fiddler AI, an enterprise AI Observability company. He’s built and led a data science team that developed novel explainability tools for computer vision and multimodal deep-learning models, and techniques for measuring model robustness and drift in unstructured data, key components of Fiddler's LLM observability product. Most recently he's been developing small BERT-scale models to close the feedback loop on measuring large language model performance, serving customers including cloud-native travel platforms, large financial services firms, ad-tech companies, and cryptocurrency exchanges.