Mitigating Hallucinations: Strategies for Enhancing Generative AI Accuracy
AI Hallucination is a phenomenon where a large language model (LLM), often a generative AI system, perceives non-existent patterns and produces outputs that are nonsensical or inaccurate.
Why Does It Happen?
LLMs aim to generate responses that appropriately address user inputs. However, sometimes these AI algorithms create outputs that:
Are not based on training data,
Are incorrectly decoded by transformers,
Do not follow any identifiable pattern.
In other words, the AI “hallucinates” the response.
Though the term "hallucination" is typically associated with human or animal brains, it aptly describes these AI-generated inaccuracies from a metaphorical standpoint.
Impact on Business
In a customer interfaced system using GenAI, hallucinations can lead to subpar customer experiences, reducing overall customer satisfaction and affecting the adoption of our system.
How to Prevent AI Hallucinations
Use High-Quality Data
Generative AI relies on input data to perform tasks, so the quality and relevance of training datasets will dictate the model’s behaviour and the quality of its outputs.
Consider RAG Implementation
Augmenting data with additional information relevant to the context can enhance the AI's ability to generate accurate responses. For instance, if developing an AI system to provide product support, it’s crucial to supply product-specific details, usage patterns, and common error cases.
Add Constraints
Imposing constraints on generative AI is vital for limiting responses. Utilize filtering tools and set clear probabilistic thresholds to guide the model.
Test and Refine Continuously
Regularly updating training datasets will improve the quality of the AI’s responses. Continuous testing and refinement are essential.
Human Oversight
Human review and validation are critical. Involving humans ensures that any AI hallucinations can be identified and corrected. Human reviewers, especially subject matter experts, can also provide valuable insights, further enhancing the AI’s learning process.
By following these steps, we can significantly reduce the occurrence of AI hallucinations and improve the reliability of our generative AI systems.
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