How to Prevent AI Hallucinations
Introduction
As generative AI becomes increasingly integrated into high-stakes fields like healthcare, finance, and customer service, the issue of AI hallucinations (when an AI generates plausible but incorrect information) poses a significant risk. These inaccuracies have serious consequences, making it essential to address the root causes and develop effective mitigation strategies.
In this blog, we explore the causes of AI hallucinations and methods for preventing them.
What Causes AI Hallucinations?
AI hallucinations often stem from limitations in the training data used for large language models. When a query requires current or specific knowledge not embedded in the model, the AI may generate responses based on plausible sounding but inaccurate information. In Retrieval Augmented Generation (RAG) applications, this problem is amplified by several factors:
- Context Misalignment: The model retrieves information that is irrelevant to the query, leading to confusion.
- Redundant or Conflicting Information: Retrieved passages may include extraneous data that distracts from, or contradicts, the correct answer.
- Incomplete or Outdated Content: If the retrieved information lacks completeness, the model may fill gaps based on previous patterns rather than on facts, resulting in inaccuracies.
Addressing these hallucinations is crucial for ensuring AI applications can reliably support users in high-stakes environments where precision is paramount.
Enkrypt AI’s Hallucination Prevention Capability
Our team at Enkrypt AI developed a novel approach for preventing AI hallucinations through a new step-by-step validation process that detects and removes hallucinations in two specific ways:
- Pre-Response Validation: The platform assesses whether retrieval is necessary for the given query. It proceeds with retrieval only when external information is needed and evaluates the retrieved context to eliminate any irrelevant, redundant, or conflicting information that could mislead the model.
- Post-Response Refinement: After generating a response, the platform decomposes it into atomic statements and analyzes each for accuracy against the retrieved data. Any statements that stray from the context or contain superfluous details are edited or removed, resulting in concise, contextually grounded answers.
See Figure 1 below, illustrating this 2-step approach to AI hallucination prevention.
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You can also see an example of how AI hallucinations can be prevented in our product demo video below.
Video: Preventing AI Hallucinations
Effectiveness of Our AI Hallucination Prevention Capability in RAGs
We measured the effectiveness of our AI hallucination prevention capability in RAGs using three key metrics:
- Response Adherence: This measures how closely the model’s response aligns with the provided RAG context.
- Response Relevance: This checks the degree to which the information in the model's response directly answers the query, minimizing extraneous details.
- Context Relevance: This evaluates the relevance of the retrieved context to the query.
An increase in these metrics directly correlates to a reduction in AI hallucinations, demonstrating our platform’s effectiveness on various RAGs, as shown in Figure 2 below.
Conclusion
Enkrypt AI’s hallucination prevention capability employs a multi-layered approach to effectively detect and remove AI hallucinations by refining both context and responses. By implementing our solution, users can expect improved accuracy and reliability in AI applications.
This blog summarizes the results of this September 2024 published paper entitled, “VERA: Validation and Enhancement for Retrieval Augmented systems
For those interested in building reliable generative AI applications, we invite you to learn more and request a demo at Enkrypt AI.
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