Posts tagged with "LLM"

The capability spectrum from stateless LLM to multi-agent orchestration is one of the most conflated concepts in the 2026 AI market. The distinctions matter. They change architecture, they change cost by an order of magnitude, and under the EU AI Act they change compliance posture.

A concrete playbook for reducing Azure OpenAI bills in 2026. Break-even math for Provisioned Throughput Units, prompt-cache economics, the Batch API 50 percent discount, Foundry IQ for retrieval, tiered model routing, and the telemetry that keeps the wins honest.

Generic LLM copilots are a liability in manufacturing. Technicians need answers that cite the exact procedure, not plausible-sounding text. Retrieval-augmented generation grounded in Azure AI Search solves this when architected correctly. This is the pattern that holds up under service-bay pressure.

Classic RAG hits a ceiling when questions require multi-hop reasoning or query refinement. Agentic RAG — treating retrieval as a tool, decomposing queries, adding self-correction loops — routinely wins where classic RAG plateaus.

Structured outputs and function calling solve overlapping problems differently. This post walks through the reliability tradeoffs, provider quirks across Anthropic, OpenAI, and Azure, and the decision rules that lead to the right pick.

A year-end, hype-free review of where agentic AI actually shipped in 2025, which patterns graduated from pilot to production, and which still need work before they are safe to trust with consequential decisions.