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AI Chatbot Errors Stem from Retrieval, Not Core Model

By PulseAugur Editorial · [1 sources] ·

AI chatbots that answer questions based on user documents often fail not because of the core AI model, but due to issues in the preceding retrieval and processing steps. Problems can arise during text extraction from documents, how the text is split into smaller chunks, the creation of numerical representations (embeddings) for meaning, or the search process that selects relevant passages. If the correct information is not retrieved and presented to the model, it may incorrectly state it cannot find the answer or hallucinate a response. AI

IMPACT Highlights common failure points in RAG systems, guiding developers to focus on retrieval and chunking for better chatbot accuracy.

RANK_REASON The item explains a common technical failure mode in AI applications without announcing a new product or research.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI Chatbot Errors Stem from Retrieval, Not Core Model

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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Tushar Bhendarkar ·

    AI Chatbot Giving Wrong Answers? Why It Can't Find What's in Your Documents

    <p>When an AI chatbot gives wrong answers about your own documents, the AI model is usually not the problem. The model only reads the few passages a search step hands it, and when that search hands it the wrong ones, a perfectly good model answers badly.</p> <p>You have probably …

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