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Don't ask your AI model to do a database's job.
Modern CTO18 September 2026Watch on YouTube
What you'll learn
- AI models are judgment engines, strong at synthesis, pattern recognition and dynamic decision making.
- Retrieval is a different problem from reasoning, and ranking is yet another problem.
- Search, ranking and reasoning are best treated as separate problems.
- Treating retrieval and reasoning as the same job leads to bad answers.
- Separate databases and models within RAG and enterprise AI architectures.
Frequently asked questions
Why should you not use an AI model for retrieval?
The model is a judgment engine that is good at synthesis, pattern recognition and dynamic decision making. Finding the right information, and finding it efficiently, is a different problem from reasoning.
Which tasks are distinguished in the video within AI architecture?
Search, ranking and reasoning are treated as separate problems. Retrieval and reasoning are not the same task.
What happens if a product treats retrieval and reasoning as the same job?
You are setting yourself up for bad answers. The video therefore advises a separation between databases and models in RAG and enterprise AI architectures.
What is an AI model good at according to the video?
Models are strong at synthesis, pattern recognition and dynamic decision making. Those qualities do not describe retrieval, because that is a different problem.
Topics
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Description from the channel
AI has a search problem. If your product treats retrieval and reasoning as the same job, you're setting yourself up for bad answers. Models are judgment engines. They're excellent at synthesis, pattern recognition, and dynamic decision making, but none of that describes retrieval. Finding the right information, and finding it efficiently, is a different problem entirely, and ranking is different again. Don't ask your model to do a database's job. #Sponsored #AISearch #RAG #EnterpriseAI