Context engineering for AI agents goes past prompts: retrieval design, chunking, memory, tool context and how each one fails.
Context Engineering for Enterprise AI Agents: Beyond Prompt Engineering
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— Blog · The Discipline Layer for AI
Field notes on doing AI the disciplined way.
Contrarian, data-grounded essays on the three things that decide whether AI pays: a sharper problem, governed data, and demand that survives the answer-engine age.
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Growth · Featured essay
AI Content Generation: From Caves to Code
The first content strategists painted on cave walls. The newest ones orchestrate agents. This essay traces the whole arc — and shows how a disciplined content engine drove 359% organic traffic growth for a B2B client by treating generation as a system, not a shortcut.
Frank Shines & Umer Qureshi · Read the essay →
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Every post strengthens one of the three foundations — Problem, Data, or Growth.
RAG Guardrails: Stopping Agents That Leak or Hallucinate in Production
RAG guardrails that stop agents leaking or hallucinating in production, mapped to the specific risk each control removes.
MLOps and AIML Operations Services: From Model to Production
MLOps and AIML operations services that move models from laptop to production. See how the AIM-IT framework closes the lab-to-live gap.
AI and ML Data Analytics Solutions: Use Cases, Stack and Outcomes
A practitioner guide to AI and ML data analytics: real use cases, the modern MLOps stack, the challenges that stall projects, and
Unity Catalog permissions and service principals in plain English
Unity Catalog permissions in plain English: grant to Entra ID groups, reuse each Databricks App’s service principal and mask PII columns.
Bronze to Gold in Practice: Making Fragmented Enterprise Data AI-Ready
A practical walkthrough to make fragmented enterprise data AI ready, from raw sources to a gold layer an agent can safely query.
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