𝗪𝗵𝘆 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 𝗔𝗿𝗲 𝗡𝗼𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝗔𝗜 𝗔𝗽𝗽𝘀

𝗪𝗵𝘆 𝗣𝗿𝗼𝗺𝗽𝘁𝘀 𝗔𝗿𝗲 𝗡𝗼𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 𝗳𝗼𝗿 𝗔𝗜 𝗔𝗽𝗽𝘀

You build a demo. It works. You ship it. Then it breaks.

The model forgets facts. Terms drift. You do not know where the data comes from.

Many builders use a simple plan.

• Collect data.
• Put it in a prompt.
• Call the model.
• Ship.

This works for a demo. It fails for a real product.

You need AI-driven data architecture. This is not a vector database. It is a system to prepare and own context. The LLM is a user. It is not the center of your system.

Think of these eight layers for your data:

• Ingest: Where truth lives.
• Extract: Finding structured facts.
• Store: Who owns each fact.
• Index: Finding the right passage.
• Synthesize: Creating summaries.
• Evaluate: Measuring if it works.
• Consume: Chat or agents.
• Improve: Feedback loops.

Lessons from the field:

• Prompting is consumption. It is not a foundation.
• SSOT boundaries matter more than the model choice.
• Measurement is a layer. Do not skip it.
• Agents need data contracts.

Do not confuse a feature with architecture. RAG is a technique. A true architecture owns the knowledge.

Part 2 will cover the blueprint.

Source: https://dev.to/letuhao/ai-driven-data-architecture-part-1-why-prompts-arent-enough-5667 Optional learning community: https://t.me/GyaanSetuAi