Date & Time
May 7, 2026 9:30 PM IST
Venue
Online
Garbage In, Hallucinations Out: Fixing AI with Better Data Engineering
AI systems do not fail because of models alone. They fail because of poor data. Inconsistent, incomplete, or outdated data leads to hallucinations, unreliable outputs, and loss of trust at scale. As enterprises move from AI pilots to production, data quality becomes the most critical factor for success.
This webinar explores how weak data foundations impact AI performance and how structured data engineering practices can fix it by enabling reliable data pipelines, reducing hallucinations, and building AI systems that deliver consistent, enterprise-grade outcomes.
What You’ll Learn
- Understand the root causes of AI hallucinations, including confabulation, factual errors, and context drift
- Apply the six key dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, and uniqueness
- Analyze real-world AI failures across legal, healthcare, and retail sectors
- Implement practical solutions such as data profiling, automated quality checks, RAG architectures, and RLHF
- Establish governance frameworks with clear data ownership, quality SLAs, and data drift monitoring
If you are building or scaling AI systems, this session will help you improve reliability, reduce risk, and drive measurable outcomes through stronger data engineering practices.
Register now to secure your spot.
Our Presenters
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