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AI EngineeringDesign

Design systems for AI products: showing uncertainty honestly

Confidence, sources, and the moment a system should admit it does not know — interface patterns for products whose output is probabilistic.

Deep Tensors TeamAug 22, 20261 min read

Conventional interfaces present results as facts. AI products present guesses, and the interface has to carry that difference without drowning the user in caveats.

Show the source, not the score

"87% confident" means nothing to most users. A citation they can open means everything.

Make correction the fastest path

If fixing the output is harder than redoing the work, people redo the work. Editing should take one click and feed straight back into your labelling queue.

Design the refusal state first

Every AI feature needs a good-looking "I don't know". Designed last, it becomes an error toast; designed first, it becomes a trust signal.

Deep Tensors Team

Engineering

Notes from the team shipping AI, web, and automation systems for companies that need them to actually work on Monday morning.

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Keep reading

AI EngineeringLLMs2 min

Why most AI agents fail in production

The demo works, the pilot stalls, the rollout quietly dies. Four failure modes we see in almost every agent project — and what a system that survives contact with real users looks like.

Ishtiyaq KhanJul 28, 2026
AI EngineeringLLMs1 min

RAG is not a product: retrieval patterns that hold up

Chunk, embed, search, stuff into a prompt. That pipeline gets you a convincing prototype and a support queue full of confidently wrong answers. Here is what we build instead.

Ishtiyaq KhanJul 14, 2026
AI EngineeringComputer Vision1 min

Computer vision on the factory floor: six lessons

Lighting changes, cameras drift, and the operators will move the mount. What we learned deploying defect detection into a plant that runs three shifts a day.

Deep Tensors TeamJun 30, 2026