IA Générative
8 articles
Applications LLM, architectures RAG et stratégie IA pour l'entreprise. Guides pratiques sur le déploiement de l'IA générative en production.
From SaaS to intelligence native: the feedback loop.
Intelligence-native systems need agent access to decision artefacts and feedback loops. Why context, not models, is the differentiator — and how MCP, traditional ML, and versioned artefacts fit together.
Who controls what your AI recommends?
Seasons, campaigns, and weekly events — retail runs on overlapping cycles, and the AI recommender has to keep up with all of them. Notes on the business-rules control surface that lets merchandising teams steer a conversational recommender without editing prompts, filing tickets, or waiting for a deploy.
Ce que le commerce agentique exige vraiment en production
Macy's annonce que les clients utilisant son assistant IA dépensent 4,75x plus. Sephora vient de lancer une app dans ChatGPT. Zalando déploie son assistant dans 25 marchés. La question pour tous les autres retailers n'est plus 'faut-il le faire ?' mais 'comment l'architecturer pour que ça tienne en production ?'
Building a recommendation engine that doesn't trust the LLM
This is the engineering companion to the production architecture piece. Instead of re-arguing why open-ended agents are risky in commerce, it walks through the actual implementation choices in `ai-florist`: FastAPI boundaries, LangGraph orchestration, pgvector retrieval, learned scoring weights, deterministic fallbacks, and runtime observability.
What agentic commerce actually requires in production
Macy's reports customers spending 4.75x more with their AI assistant. Sephora just launched inside ChatGPT. Zalando rolled out to 25 markets. The question for every other retailer isn't whether to build this — it's how to architect it so it actually works. Here's what I learned building one for premium floral e-commerce.
LLM Systems: Architecture First, Framework Second
LangChain, LangGraph, Claude ADK, Google ADK, PydanticAI — different names, different abstractions, same promise. This article cuts through the framework noise and identifies the five architectural properties that actually determine whether an LLM system survives production.
Pourquoi les projets d'IA agentique échouent: un problème d'operating model pas seulement de technologie
La plupart des échecs GenAI en entreprise ne viennent pas d'un mauvais modèle. Ils viennent d'un mauvais design de workflow, d'une absence de mandat et d'une architecture incapable de faire agir un système.
Why Smarter AI Does Not Automatically Mean Better Architecture
Modern LLMs are objectively better at nuanced language tasks than legacy systems. But when you zoom out from a single prompt in a playground to a production environment processing millions of requests, the paradigm shifts entirely. Here is why using an LLM as a universal parser and router is often the wrong architectural choice.