Building an AI recommendation system — developer case study

Pradyumna Kodgi’s case study on Mind the Product walks through the complete product lifecycle of building an AI recommendation system for developers — from identifying the problem through deployment and iteration.

The problem

Developers in the target organization spent significant time on repetitive tasks: choosing integration patterns, selecting libraries, and configuring environments for common scenarios. The knowledge existed across documentation, past projects, and senior engineers’ heads, but accessing it required searching or asking. The AI recommendation system was designed to surface relevant guidance at the point of need.

The product process

What makes this case study valuable is its focus on the product decisions rather than the technical implementation. Kodgi describes the discovery process — understanding which recommendations developers would trust and which they would ignore. The finding was counterintuitive: developers trusted AI recommendations for routine decisions (library versions, configuration templates) but resisted them for architectural choices, even when the AI was accurate.

Model selection was driven by product requirements, not technical preferences. The team chose approaches based on explainability (developers wanted to know why something was recommended) and confidence scoring (recommendations came with uncertainty indicators that helped developers calibrate their trust).

User testing revealed that the presentation of recommendations mattered as much as their accuracy. Displaying reasoning alongside suggestions increased adoption significantly compared to bare recommendations.

Lessons for PMs

The central lesson is that building an AI feature for technical users requires earning trust through transparency. Confidence scores, explanations, and the ability to override suggestions are not nice-to-haves — they determine whether the feature gets used or abandoned.

Who should read this

PMs building AI features for developer or technical audiences, and any PM interested in how trust dynamics affect AI product adoption.