Handling Hallucinations in LLM-Powered Applications
The article discusses the challenge of what to do after detecting hallucinations in LLM-powered applications, as the appropriate response depends on the specific context and use case.
Why it matters
Handling hallucinations is a critical challenge for deploying LLM-powered applications in production, and this article provides a framework to address this issue.
Key Points
- 1Detecting hallucinations is easier than deciding how to handle them correctly
- 2Different applications (customer support, legal, coding) require different policies when hallucinations are detected
- 3The author proposes opinionated default policies (block, retry, flag) with overridable hooks for customization
Details
The article highlights that while detecting hallucinations in LLM outputs is an important first step, the harder problem is determining the appropriate action to take after detection. The author notes that the right response depends on the specific application context - for example, a customer support bot may want to retry with a more conservative prompt, while a legal document analyzer should block and escalate to a human. The author proposes a framework with three built-in policies (block, retry, flag) that can be overridden based on the application's needs. This allows teams to have safe defaults out-of-the-box while also providing flexibility to customize the handling of hallucinations for their specific workflows.
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