A WhatsApp assistant for an internet service provider was developed to answer billing questions, but encountered issues not with retrieval accuracy, but with the language model misrepresenting correct numerical data. The system employs a hybrid retrieval method combining pgvector for semantic search and PostgreSQL's full-text search for specific identifiers, fused using Reciprocal Rank Fusion. To address the model's tendency to hallucinate or misstate numbers, a grounding verifier and a humanizer step were implemented to catch and correct errors before sending a response to the user. AI
IMPACT Highlights the critical need for robust grounding and output verification in LLM applications, especially when dealing with factual data like billing information.
RANK_REASON The item describes the implementation and challenges of a specific application of RAG for a business use case, rather than a novel model release or research breakthrough.
- internet service provider
- pgvector
- PostgreSQL
- Reciprocal Rank Fusion
- retrieval-augmented generation
- Yan
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