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AI-Native Startup
LLM feature integration with evaluation harness
Added an AI-assisted feature to an existing product, including prompt design, retrieval, and an evaluation suite to catch regressions before release.
PythonNext.jsVector DBLLM APIs
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Demo · rule-based, not a live model
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Approach
- Defined cost and latency budgets before picking a model
- Built a retrieval layer scoped to the product's actual data, not the open web
- Added an evaluation harness so prompt changes can't silently regress
Typical outcome
Shipped with cost and latency budgets defined up front, not discovered in production.
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