A retrieval-augmented generation solution for complex, contradictory, or evolving document sets
Build an AI question-answering system that retrieves relevant documents from a vector database and passes them, along with the user's question, to an LLM. The system must account for obsolete documents, ambiguous questions, contradictory sources, and information that represents opinion rather than fact.
online B2B Service / Consulting / Agency
From IBM Technology — Why RAG Solutions Fail with Complex Documents & Vector Databases at 00:14
Problem: Prevents confusing or misleading AI answers caused by outdated documents, contradictory sources, vague questions, and opinions being presented as facts.
For: Organizations managing complex repositories such as laws, policies, agency regulations, or legal opinions.
Examples
- A repository may refer to LSU as both the 2019 and 2020 NCAA football champion because football seasons and championship games span different calendar years.
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