Prompt and context design for Claude batch jobs at scale
What to put in context for Claude Message Batches: frozen vs volatile prefixes, caching, few-shot examples, and per-item model routing.
Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
What to put in context for Claude Message Batches: frozen vs volatile prefixes, caching, few-shot examples, and per-item model routing.
Use tools and MCP servers in Claude Message Batches safely: strict schemas, auth, error handling, and custom_id-based idempotency across many requests.
Code-level patterns for Claude batch processing: request factories, cache-friendly context layering, structured outputs, and self-describing custom_ids.
Hands-on Claude Message Batches walkthrough: shape requests, submit, poll to ended, reconcile counts, and reassemble results by custom_id in Python.
How Anthropic's Message Batches API works end to end: async queue, per-request isolation, the 50% discount, caching, and the 29-day result store.
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Windsurf coding agent: a practitioner's comparison of the leading AI coding agents — Cursor, Windsurf, and Claude Code — covering architecture, capabilities, pricing, and which tool fits different workflows.
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Google Cloud case studies show AI agents delivering 3x-6x ROI within first year of deployment. Real enterprise results and implementation patterns.