The Primix Labs journal
Notes on building useful AI.
Practical ideas on generative AI, agentic systems, and the engineering that makes intelligent software dependable in the real world.
Before You Add an AI Agent, Map the Workflow
A quick way to tell whether a task needs an agent—or a simpler, more predictable automation.
Agentic AI in Production: Start with a Bounded Loop
A practical architecture for agents that use tools safely, stay observable, and know when to hand work back to a person.
RAG Is a Product System, Not a Vector Database
Reliable retrieval-augmented generation depends on data quality, access control, evaluation, and a thoughtful answer experience.
From Generative AI Demo to a Reliable Product
The engineering work that turns an impressive GenAI prototype into a feature users can trust day after day.
Human-in-the-Loop Is a Product Design Choice
Thoughtful review and escalation are part of a useful agent experience—not just a last-minute safety control.
How to Evaluate an LLM Feature Beyond a Happy-Path Demo
Build an evaluation habit that catches brittle answers, missing context, and workflow regressions before users do.
RAG or Fine-Tuning? Choose the Tool for the Change You Need
How to decide whether a generative AI application needs better access to knowledge, a change in behavior, or both.
Your First GenAI Feature Should Be Small Enough to Measure
A practical way to choose an initial generative AI feature, define success, and learn before expanding its scope.