True, Relevant, and Wrong: The Applicability Problem in RAG (www.pinecone.io)

A recent analysis highlights a critical challenge in Retrieval Augmented Generation (RAG) systems: the "applicability problem." While RAG technologies are marketed as solutions to reduce AI hallucinations by grounding responses in verified documents, the complexity increases as the corpus of information expands. As organizations accumulate diverse policy documents influenced by various conditions (e.g., region, eligibility, product versions), the risk arises that the AI may provide a confidently cited answer that is technically accurate yet contextually wrong—leading to what is termed a "franken-answer." This occurs when the generated response blends multiple pieces of true information without selecting the applicable context, making it incoherent for real-world application. The significance of this issue for the AI/ML community lies in its implications for the design and optimization of RAG systems. Traditional retrieval methods focus on topical relevance rather than the contextual correctness necessary for accurate applicability. To combat these issues, future developments should emphasize understanding the scope of information, incorporating eligibility criteria, and recognizing authority in the retrieved texts. Failure to address the intricacies of applicability could lead to costly errors in customer interactions across multiple sectors, highlighting a need for advanced methodologies in AI-driven support and decision-making.