Your AI Feature Needs a Data Plan Before You Add the LLM #aidevelopment

Ajouté : 2026-05-21

[00:00:00]Most AI features fail before the model even runs.

[00:00:04]The real problem is not the LLM. It's the missing data plan.

[00:00:09]If your team adds ChatGPT to a product without defining what data it can access, how it retrieves answers, and what happens when retrieval fails, you're building a demo, not a system.

[00:00:22]That's why so many AI integrations look smart in testing and then break in production.

[00:00:28]Before code, decide three things.

[00:00:31]One, what sources are allowed.

[00:00:34]Two, how data is structured for retrieval. And three, the fallback path when the answer is missing or low confidence.

[00:00:43]That can mean a curated knowledge base, embeddings with metadata filters, and a default response like, "I couldn't verify this. Here's the next best step."

[00:00:54]Imagine a customer support bot for enterprise pricing.

[00:00:58]If it only uses the LLM, it may invent policy.

[00:01:02]If it retrieves from approved docs and falls back to a human handoff when confidence is low, you get something safer and actually usable.

[00:01:11]So, don't start with prompts.

[00:01:13]Start with access, retrieval, and fallback logic.

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