Howsoftwareactuallyships.
Field notes on building AI into real products — RAG, chatbots, agents, and the architecture decisions that outlive a release. Written by the team that ships them.
Notes from thedelivery floor.
Retrieval pipelines that return the wrong passage with total confidence. Chatbots that pass a demo and fail a customer. The moment a model stops answering and starts acting, and what that changes about who can approve what. These are the problems that show up once an AI feature meets production, and the tradeoffs behind each one.
Alongside them: the engineering decisions that outlive a release — how to version an API before anyone depends on it, which technical debt compounds and which is safe to carry, and when blockchain is genuinely the right tool rather than a database with extra steps.
- AI in production
- RAG, retrieval quality, hallucination, agents, and prompt injection — what holds up once real users and real documents are involved.
- Architecture calls
- The decisions with a long tail: model selection, API contracts, cross-platform mobile, and the debt worth paying down.
- Industry context
- How the same technology lands differently in healthcare, fintech, EdTech, and e-commerce — where the constraints actually bind.
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