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This article details a Python toolkit that structures Large Language Model interactions into a six-stage pipeline—spanning business understanding, SQL planning, and validation—to prevent AI from hallucinating insights based on insufficient data. By enforcing a discipline where the system checks data volume before generating recommendations, the approach effectively simulates the rigorous analytical workflow of a senior human analyst using standard APIs like Anthropic or OpenAI.

Sources coveringKDnuggets, IEEE Spectrum, SiliconANGLE

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Build an AI Data Analyst That Thinks Like a Senior Analyst

KDnuggetsSeptember 9, 20267 min read

A six-stage pipeline that checks its numbers before calling anything an answer.

Ask a chatbot "which promotion should we run more of," and it answers in one breath. It picks a number, states it with confidence, and stops. It picks the promotion with the best-looking number and states its choice confidently. But it may never check how much data that number is based on. A promotion that looks great after 10 orders is much less convincing than one that performs well across 1,000 orders. A senior analyst works slower on purpose. They restate the question, form a hypothesis, write the query, then check whether the result has enough data behind it before they say anything to an executive. We can build that discipline into code. In this walkthrough, we build a small Python toolkit that pushes a question through six stages instead of one prompt:…

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