Summary: In 2025 the center of gravity moved from model demos to production outcomes. Teams winning with AI share three traits: disciplined scoping, great retrieval, and ruthless measurement.…
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Why context engineering? LLMs are sensitive to the quality and structure of their inputs. Context engineering is the discipline of preparing, optimizing, and governing the corpus that prompts…
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“Agents” and “agentic workflows” are often conflated. In practice they optimize for different things. Definitions Single‑purpose agent: a task specialist with fixed tools and a narrow contract…
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Retrieval quality degrades silently. The culprit is often embeddings and vector search, not the LLM. Here’s a monitoring strategy that catches issues before users do. Failure modes to…
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Model performance matters, but platform simplicity and cost control often decide success. This guide outlines a pragmatic stack and guardrails. Principles Build on the data platform your…
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