Anthropic

Claude models bring strong long-form reasoning and a disciplined instruction-following character to production AI. I use the Anthropic API where long context and careful reasoning earn their keep.

My work covers the full integration surface — messages API, tool use, structured output, and the context/document handling that makes Claude strong with real business material.

The same engineering discipline applies as with any frontier model: typed contracts, evals, fallbacks, and cost/latency control so the integration stays dependable as the platform evolves.

Long context is where Claude earns its place. When the task needs substantial past conversation or a large document as grounding, the API’s context handling keeps the model coherent — which is the right tool for nuanced reasoning over real material.

Instruction-following discipline is a real, usable strength. Claude responds well to precise, structured prompts, so I get dependable structural output and consistent behaviour when the contract is defined clearly.

Tool use composes for agentic builds. I present the model a typed tool surface and it decides when to call them — with limits and approval on anything destructive — so Claude drives multi-step work within a governed boundary.

Document and knowledge handling make it strong for retrieval. Feeding context alongside the query keeps answers grounded, which slots Claude cleanly into RAG where the source material must inform the response.

Quality is measured, not assumed. I tie prompts to an eval set and add fallbacks with output validation, so a platform update cannot silently degrade behaviour — the same discipline as any critical dependency.

Provider routing keeps the integration swappable. Behind a stable interface I can move between Claude, OpenAI, and Gemini as cost or capability dictates, which is how a production AI surface stays resilient rather than captive.

Long context is where Claude earns its place. I use it where the task needs substantial history or a large document as grounding, and the API's context handling keeps the model coherent over it.

Instruction-following discipline becomes a usable strength. Claude responds well to precise, structured prompts, so I get dependable structural output when the contract is defined clearly.

Tool use composes for agentic builds. I present a typed tool surface and the model decides when to call it, with limits and approvals on anything destructive.

Document and knowledge handling make it strong for retrieval. Grounding the answer with context keeps it anchored, which slots Claude cleanly into RAG.

Quality is measured, not assumed. Prompts are tied to an eval set and paired with fallbacks and output validation, so a platform update cannot silently degrade behaviour.

Cost and latency are engineered per call. I budget tokens, route simple work to a cheaper tier, and cache repeated requests.

A stable provider layer keeps the integration swappable. Claude, OpenAI, and Gemini can be chosen by cost or capability behind one contract.

Observability covers the model. Tracing, sampling, and per-call token and latency metrics make the LLM a governed component.

I use Claude where careful reasoning and long context genuinely pay — not as the default for every prompt in the product.