Microsoft AI-103: RAG, Vector Search, and Grounding
Retrieval-augmented generation is central to AI-103 because it solves a practical limitation of generative models: the model should not be…
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Retrieval-augmented generation is central to AI-103 because it solves a practical limitation of generative models: the model should not be…
Model selection in AI-103 is not a contest to pick the largest model. Microsoft’s blueprint explicitly expects candidates to choose…
Choosing Foundry services is one of the first engineering decisions in Microsoft AI-103, but the interesting question is what each…
Professional architecture is largely the discipline of choosing what not to add. In CCAR-P, several answers may be technically possible,…
Enterprise integration is where a Claude architecture stops being a model demo and becomes part of a real operating environment.…
Multi-agent architecture is attractive because complex work can be divided among specialized Claude instances, but specialization is not free. For…
A production Claude system is not observable merely because it logs API errors. For CCAR-P, observability means the team can…
Evaluation is the mechanism that turns an AI architecture from opinion into evidence. For CCAR-P, a professional architect should be…
Safety architecture begins with a simple question: what can go wrong if Claude misunderstands the user, trusts hostile content, or…
Security and governance are not layers that can be attached after a Claude system has already been designed. For a…
Context is Claude’s working set for a request: instructions, conversation history, tool definitions, tool results, documents, and other content the…
Tool use turns Claude from a system that only generates content into one that can retrieve live information, call business…