Technical depth on AI, Azure, Next.js, and the engineering decisions behind them.
Industrial documents arrive as scanned paper, multilingual procedures, and proprietary CAD exports. Azure AI Search skillsets turn them into queryable text with structured metadata. This post walks the OCR, entity, language, and custom skill patterns that make the difference between a searchable index and one that actually answers technical queries.
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.
A production observability stack for LLM applications on Azure — wiring OpenTelemetry, Langfuse, and Application Insights together so traces, token usage, eval scores, and platform metrics land in the same view.
ISO 9001 and IATF 16949 require controlled, traceable, and revisable documentation. A modern Azure AI Search pipeline can automate ingestion, audit-trail generation, and revision tracking without compromising the compliance posture. The patterns that work for aerospace, automotive, and medical device manufacturers share a common shape.
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.
Automotive technical documentation is uniquely structured: ECU specs, wiring diagrams, service manuals, diagnostic codes, recall notices — all layered across vehicle platforms, model years, and markets. Azure AI Search handles this at scale if the index schema and retrieval pattern match the domain. This post walks the automotive-specific design choices.
When running small language models on-prem actually beats hosted inference — Phi-4, Llama 3.3, GPU sizing, Ollama and vLLM deployment patterns, and the honest cost math for Swedish data-residency workloads.
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.
Pure keyword search misses semantic matches. Pure vector search misses exact part numbers. Hybrid search combines both. This post explains the rank fusion and semantic re-ranking choices that turn a hybrid Azure AI Search query from "works on demos" into a production retrieval layer for manufacturing knowledge bases.
A side-by-side comparison of DeepEval, Promptfoo, and LangSmith for evaluating agentic AI systems in 2026 — on metrics, tooling, CI integration, agent-specific evaluation, and when each is the right pick.
A technical guide to prompt caching across Claude, Azure OpenAI, and GPT — what belongs in the cache, how to structure cache breakpoints, TTL realities, hit-rate optimization, and the anti-patterns that erase the savings.