A Deep Dive into Factual & Consistency Checks
Factual accuracy and consistency often degrade quietly as models, prompts, and retrieval sources evolve. This workshop focuses on making factual reliability measurable, testable, and enforceable in real systems.
To win, your GenAI solutions must reliably produce factually grounded outputs that remain stable across versions and deployments.
When factual and consistency checks are underdeveloped, teams lose control over reliability as systems evolve.
• Undefined truth: Teams lack clear benchmarks for what “factual” and “consistent” actually mean in their domain.
• Weak verification: Fact-checking datasets and metrics are insufficient to detect drift, hallucinations, or regressions.
• Hidden instability: Model updates and retrieval changes introduce inconsistencies that go unnoticed until users report issues.
These failures erode trust, increase rework, and make it risky to ship new model versions.
In this hands-on workshop, your team designs and evaluates factual and consistency checks using structured methods and applied exercises.
• Define factual consistency benchmarks aligned to your use cases and risk tolerance.
• Build fact-checking datasets and metrics that surface errors and regressions.
• Integrate RAG and retrieval systems to ground outputs in verifiable sources.
• Measure output stability across model versions and configuration changes.
• Flag and track inconsistencies using versioning and comparison tools.
- Defining Factual Consistency Benchmarks
- Building Fact-Checking Datasets and Metrics
- Integrating RAG and Retrieval Systems
- Measuring Stability Across Model Generations
- Flagging Inconsistencies with Versioning Tools
• Define what factual accuracy and consistency mean for their GenAI systems.
• Create datasets and metrics that reliably detect factual errors.
• Ground model outputs using retrieval and source-based validation patterns.
• Identify instability introduced by model or system updates.
• Apply versioning tools to flag and manage inconsistencies over time.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
RAG pipelines, evaluation datasets, and versioning comparison tools