PROJECT BRIEF / 01
From RAG to release-ready
Operational LLMOps with Microsoft Foundry
The context
An existing RAG chatbot is only the starting point. Customer teams need a practical way to evaluate quality, compare model changes, and decide what is ready to move forward.
The approach
- Use a pre-built RAG chatbot to make the operational lifecycle concrete.
- Introduce automated evaluation for groundedness, relevance, similarity, and fluency.
- Compare model versions and re-evaluate before promotion.
- Connect evaluation and safety checks to Azure DevOps release gates and MLflow GenAI integration.
The leadership connection
Customer enablement that makes production-readiness decisions tangible, rather than treating model selection as the entire solution.
What to look at
Start with the architecture
Review the RAG flow, inference integration, and evaluation modules.
Follow the operational journey
Explore evaluation workflows, model swaps, and CI/CD promotion gates.
Adapt before using
Define your own evaluation set, acceptance criteria, identity controls, and operating requirements.
Continue exploring