AI 🤖
NovaGPT
AI · LLM applications
By Arush Bansal
Useful without the theatre
Most AI demos optimize for wow. NovaGPT optimized for repeat use — clear inputs, inspectable outputs, and guardrails that made failures boring instead of catastrophic.
If users cannot tell what the model did, they cannot trust it.
The stack mixed agent orchestration with retrieval: chunking and embedding documents, routing queries to the right tools, and keeping humans in the loop only where judgment actually mattered. Interfaces were deliberately plain — chat where chat fit, forms where structure helped, and citations where facts had to be checked.
Patterns that stuck
Three principles I still use on AI products:
- 01
Retrieve before you riff
Ground answers in sources the user can open. RAG is not magic — it is disciplined context management.
- 02
Agents need budgets
Steps, tokens, and tool calls should have limits. Unbounded agents feel smart until they loop or leak cost.
- 03
Design for failure
Empty retrieval, tool timeouts, and refusals should degrade gracefully with copy users understand.
The best AI interface disappears — you notice the outcome, not the model.
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