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Rudrendu Kumar Paul

As seen in: Medium, Frontiers, Flipboard, HackerNoon, freeCodeCamp, DZone, Data Science Central, Martech Zone, pv magazine USA, Datafloq - Driving Innovation and
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Articles

Product Experimentation with Uplift Modeling: Targeting Your LLM Feature Rollout to Users Who Actually Benefit (Python Implementation)

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By Rudrendu Kumar Paul
| freeCodeCamp
Your LLM product experiment just came back positive, with a promising 8-percentage-point lift in task completion. You ship the feature and leadership celebrates. Three months later, the core metric has barely moved. The experiment was statistically sound. It simply answered the wrong question. An average treatment effect compresses the entire treatment response across your user base into a single number. That compression is useful when you're deciding whether to build a feature in the first place.
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Product Experimentation: Stop Early Without P-Hacking Using mSPRT and Sequential Testing in Python

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By Rudrendu Kumar Paul
| freeCodeCamp
Your AI product experiment reaches statistical significance on day 14 of a planned 30-day run, measuring a causal inference question: did the LLM-based feature genuinely improve outcomes? Every product manager in the room wants to ship. Your statistician says to wait the full 30 days, or the p-value is invalid. You wait. On day 30, the effect is still there. But you spent 16 days running a feature you already knew worked with 95% confidence, delaying the next experiment and burning opportunity cost.
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Product Experimentation for LLM Platforms: Switchback Designs When User Randomization Breaks Market Equilibrium in Python

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By Rudrendu Kumar Paul
| freeCodeCamp
Your team ships an intelligent query-routing feature for an LLM SaaS platform. The feature reads each incoming request in real time and decides whether to send it to the fast standard model or the more capable premium model. In offline evaluation, it raises task completion rates by six percentage points. You're ready to test it in production. Then your platform engineer raises a structural problem: you can't randomize at the user level.
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