AI/ML: Building Production AI Applications

Build, review, and architect applications that use AI models - from single-API calls to multi-agent systems with RAG pipelines. The goal is production-grade AI apps that are reliable, cost-effective, and don't hallucinate their way into an incident.

Target versions: May 2026 snapshot. Read references/target-versions.md before pinning SDKs, runtimes, vector stores, or evaluation tools.

When to use

• Integrating LLM APIs (Anthropic, OpenAI, etc.) into applications
• Building RAG pipelines (chunking, embedding, retrieval, generation)
• Designing agent systems (tool use, loops, state, multi-agent)
• Choosing between fine-tuning, RAG, and prompt engineering
• Setting up vector stores for semantic search
• Implementing structured output and tool use / function calling
• Building evaluation and testing harnesses for AI features
• Optimizing token costs, latency, and model routing