1. Hallucinations, or false information generated by AI models, can be a major issue for businesses wishing to use the technology.
2. Retrieval augmented generation (RAG) is a technical approach that aims to eliminate hallucinations by providing context for the AI model to generate accurate information.
3. While RAG can be beneficial in certain scenarios, it has limitations and can be costly to implement at scale. Further research is needed to improve the effectiveness of RAG in addressing AI’s hallucinatory problems.
Hallucinations, or inaccuracies, are a common issue with generative AI models, which are essentially predicting data based on a private schema. This can lead to major problems for businesses trying to integrate this technology into their operations. For example, Microsoft’s generative AI once invented meeting attendees and suggested discussions that never took place.
To address this issue, some generative AI vendors are advocating for a technical solution called retrieval augmented generation (RAG). This approach, pioneered by data scientist Patrick Lewis, involves retrieving relevant documents to provide additional context for the model to generate more accurate responses.
Although RAG can help reduce hallucinations and increase transparency in AI-generated responses, it is not foolproof. The technology is most effective in knowledge-intensive scenarios where relevant documents can easily be identified using keyword searches. However, reasoning-intensive tasks, such as coding and math, present more challenges in retrieving relevant documents and generating accurate responses.
Implementing RAG also comes with its own set of limitations and costs, particularly in terms of the hardware required to store and process retrieved documents. Ongoing research efforts are focused on improving model training to make better use of RAG-retrieved documents, particularly for more abstract generation tasks. Ultimately, while RAG can help mitigate hallucinations in generative AI models, it is not a one-size-fits-all solution for all of AI’s challenges.