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, as the wrong response can have significant consequences.
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 challenge is determining the appropriate response. Depending on the application, the right action could be retrying with a more conservative prompt, blocking the response and escalating to a human, or simply flagging the low-confidence output. The author suggests an approach with built-in default policies that can be overridden based on the specific context and use case. This allows teams to get safe defaults out-of-the-box, while also providing flexibility to customize the handling of hallucinations for their unique workflows.
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