---
title: "Discussion on \"Part 2: When the Sandbox Goes Blank — What BigQuery Taught Me About AI, Sales Forecasting, and the Limits of Vibe-Coding\" | Hashnode"
description: "Discussion on \"Part 2: When the Sandbox Goes Blank — What BigQuery Taught Me About AI, Sales Forecasting, and the Limits of Vibe-Coding\". Clean data pipeline in hand. Now what? The next four weeks turned a business question into a lesson about what AI genuinely can and can't do."
url: "https://hashnode.com/posts/part-2-when-the-sandbox-goes-blank-what-bigquery-taught-me-about-ai-sales-forecasting-and-the-limits-of-vibe-coding/6a4630de939f0c5fd5558bc6"
---

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Discussion

# [Part 2: When the Sandbox Goes Blank — What BigQuery Taught Me About AI, Sales Forecasting, and the Limits of Vibe-Coding](https://myprompts2production.hashnode.dev/part-2-when-the-sandbox-goes-blank-what-bigquery-taught-me-about-ai-sales-forecasting-and-the-limits-of-vibe-coding?utm_source=hashnode&utm_medium=feed)

Clean data pipeline in hand. Now what? The next four weeks turned a business question into a lesson about what AI genuinely can and can't do.

Jul 2 · 9 min read · myprompts2production.hashnode.dev  
Updated Jul 15  
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## Written by

[Ryan C](https://hashnode.com/@razr37)

@razr37

  
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## Responses 1

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[Mateo Ruiz](https://hashnode.com/@mateoruiz)

[Jul 2](https://hashnode.com/posts/part-2-when-the-sandbox-goes-blank-what-bigquery-taught-me-about-ai-sales-forecasting-and-the-limits-of-vibe-coding/6a4630de939f0c5fd5558bc6/comment/6a4647fff56739b81b4e8059)

The biggest takeaway here isn't about BigQuery or sales forecasting it's about how to work with AI on real systems.

A pattern we've started following is "sample first, build second." Before asking an LLM to generate SQL, integrations, or transformations, pull a handful of real rows and let the model reason from the actual schema instead of assumptions. It avoids hours of debugging code that's technically correct but built on the wrong mental model.

The other point that resonated is that AI is excellent at accelerating analysis, but domain expertise is still what separates an interesting output from a useful business decision. Models can surface anomalies; people provide the context that turns those anomalies into action.