Blog
I write about Java, software architecture, design patterns, applied artificial intelligence, RAG, AI agents and software engineering. Through my articles, I share feedback, technical analyzes and concrete approaches to design robust, maintainable and useful applications.
From Code to Actionable Specifications
An agent can produce clear documentation in minutes. Turning that analysis into actionable specifications, able to separate the observed from the inferred and the existing system from the target, requires a different discipline.
Modernizing a Legacy System with AI: Understanding Before Transforming
Exploring a code repository with an agent has become fast. Understanding the behaviors, rules, and constraints to preserve before transforming a legacy system is still an engineering problem, grounded in evidence rather than assumptions.
Comparing Two Versions of an LLM System
Identify and qualify behavioral changes in an LLM system when the model, the prompt, the tools, or the rules evolve, to tell an improvement apart from a regression.
Building a Representative Evaluation Set
Build an evaluation corpus that represents an LLM system's usages, edge cases, and invariants, to give version comparisons a solid foundation.
Detecting and Qualifying Behavioral Drift
Tell an acceptable behavioral drift apart from a real regression in an LLM system, by qualifying it through nature, frequency, scope, and impact rather than a single overall score.
Testing Decisions Rather Than Wording
Verify the decisions and invariants an LLM system upheld rather than the exact wording of its response, to build tests that survive changes to the model and the prompt.
Defining the Behavioral Invariants of an LLM System
Turn the behaviors an LLM system must preserve into explicit, observable, and verifiable properties, so we can tell what may vary from what must remain stable.
