AI has made remarkable strides, but challenges persist. One significant issue is the propensity of AI models to generate incorrect or misleading information, often termed “hallucinations.” This undermines trust in AI and limits its potential applications. Retrieval-Augmented Generation (RAG) emerges as a promising solution to this problem. By combining the strengths of traditional information retrieval with the power of generative AI, RAG has the potential to revolutionize AI by significantly improving the accuracy and reliability of AI-generated content. First let’s have and understand more about AI hallucinations.
What are AI hallucinations?
AI hallucination is a phenomenon wherein a large language model (LLM)—often a generative AI chatbot or computer vision tool—perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate.
Generally, if a user makes a request of a generative AI tool, they desire an output that appropriately addresses the prompt (i.e., a correct answer to a question). However, sometimes AI algorithms produce outputs that are not based on training data, are incorrectly decoded by the transformer or do not follow any identifiable pattern. In other words, it “hallucinates” the response.
The term may seem paradoxical, given that hallucinations are typically associated with human or animal brains, not machines. But from a metaphorical standpoint, hallucination accurately describes these outputs, especially in the case of image and pattern recognition (where outputs can be truly surreal in appearance).
AI hallucinations are similar to how humans sometimes see figures in the clouds or faces on the moon. In the case of AI, these misinterpretations occur due to various factors, including overfitting, training data bias/inaccuracy and high model complexity.
Preventing issues with generative, open-source technologies can prove challenging. Some notable examples of AI hallucination include:
- Google’s Gimini chatbot incorrectly claiming that the James Webb Space Telescope had captured the world’s first images of a planet outside our solar system.1
- Microsoft’s chat AI, Sydney, admitting to falling in love with users and spying on Bing employees.2
- Meta pulling its Galactica LLM demo in 2022, after it provided users inaccurate information, sometimes rooted in prejudice.3
While many of these issues have since been addressed and resolved, it’s easy to see how, even in the best of circumstances, the use of AI tools can have unforeseen and undesirable consequences.
Next let’s understand how RAG works.
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an innovative AI architecture that combines the strengths of traditional search with the capabilities of large language models (LLMs). Unlike traditional LLMs that rely solely on the data they were trained on, RAG systems can access and process external information in real time.
This two-step process involves:
- Retrieval: Relevant information is fetched from external sources like databases, knowledge graphs, or the web based on the user’s query.
- Generation: The retrieved information is integrated into the LLM’s response generation process, enhancing the accuracy, relevance, and factuality of the output.
By grounding the LLM’s responses in real-world data, RAG helps to mitigate the risk of hallucinations, where the model generates incorrect or misleading information. This makes RAG a powerful tool for applications that require accurate and up-to-date information, such as customer service, journalism, and education.
How Does Retrieval-Augmented Generation (RAG) Work?
RAG operates in a two-step process:
- Retrieval
- Query Formulation: The system generates a query based on the user’s input or the desired output.
- Information Retrieval: Relevant information is fetched from external sources such as databases, knowledge graphs, or the web using advanced search algorithms. This retrieved data can include text, images, or structured information.
- Generation
- Context Enrichment: The retrieved information is processed and integrated into the LLM’s context. This provides the model with additional knowledge and context to inform its response.
- Response Generation: The LLM generates text based on the original prompt and the enriched context. This process leverages the LLM’s ability to understand and generate human-like text.
Key benefits of RAG:
- Improved accuracy: By grounding responses in factual data, RAG reduces the likelihood of hallucinations.
- Enhanced relevance: The retrieved information helps the LLM generate more relevant and informative responses.
- Increased flexibility: RAG can be adapted to various domains and use cases by changing the underlying data sources.
By combining the strengths of information retrieval and generative AI, RAG offers a powerful approach to creating more accurate, informative, and reliable AI-generated content.
So, how can RAG help with AI hallucinations?
RAG offers a significant advantage in mitigating AI hallucinations. By grounding responses in factual data retrieved from external sources, RAG reduces the likelihood of the model fabricating information. When an LLM encounters a query, it can access relevant information from a database or knowledge graph, ensuring that its response is anchored in real-world data. This approach significantly enhances the accuracy and reliability of AI-generated content, making it a valuable tool for combating the issue of hallucinations.
