DSPy Framework mit Python: Prompts automatisch optimieren (Praxis-Guide 2026)
DSPy 3 kompiliert Prompts aus Signatures, Modulen und Optimizern. Praxis-Guide mit MIPROv2, RAG-Beispiel, Kosten und Stolperfallen aus produktiven Pipelines.
Marcus has been gluing systems together for twelve years - first as an integrations engineer at Tray.io, then four years at MuleSoft (post-Salesforce acquisition) leading a team that built connectors for regulated-industry customers. He moved full-time into LLM orchestration in 2023 after a side project - an n8n workflow that triaged his consulting firm's intake email - replaced an actual headcount. He focuses on the boring middle layer: idempotent webhook receivers, dead-letter queues for tool-call failures, and getting Temporal to play nicely with OpenAI's Assistants API. He's published two open-source n8n community nodes (one for Pinecone hybrid search, one for Anthropic prompt caching) and contributed retry-backoff improvements to the LangChain JS repo. Lives in Atlanta. Writes about what actually breaks in production agents, not what looks good in a demo.
DSPy 3 kompiliert Prompts aus Signatures, Modulen und Optimizern. Praxis-Guide mit MIPROv2, RAG-Beispiel, Kosten und Stolperfallen aus produktiven Pipelines.