Few-shot prompting for WordPress code you can ship
Zero-shot prompts make the model guess your coding standards. Feeding it two or three working examples from your own code gets output that matches your prefixes, namespaces and security patterns.
Tag: Agentic Commerce
Context engineering beats writing longer prompts
Prompting stops scaling as soon as a task has real edge cases. Here is what the Stanford ACE research found about its generator, reflector and curator loop, where the gains show up, where they do not, and how to keep the same kind of playbook in WordPress.
Your agent picks the wrong tool because of your MCP schema
Agents pick the tool with the longer description, not the right one. What that length bias looks like in a WooCommerce integration, how to keep internal arguments out of the prompt, and why a proxy beats editing someone else’s MCP server.
Agentic AI workflows that survive a failed API call
A client’s research pipeline kept hallucinating specs until we stopped writing one giant prompt. Notes on using IntelliNode’s VibeFlow to compile intent into a task graph, plus the code we run in production.
Why I moved a client’s agent to the NeMo Agent Toolkit
A client’s weekend-built AI agent fell apart the moment real users touched it. Here is what the NeMo Agent Toolkit handled for us that my own custom wrapper could not: YAML config, grounded tools, and a server you get for free.
Effective AI programming is a context problem
An AI coding agent invented table names on a 50k SKU WooCommerce sync until I wrote the schema down for it. Here are the context files I now keep in every project, and why decisions made in meetings need to end up in the repo too.
Agentic AI gives you a dot, not an ROC curve
A health-tech client had 99.8% accuracy on a disease that shows up in 0.2% of patients. Their agent said no to everyone. Here is how I pulled a continuous risk score out of a binary agent so the AUC comparison against their XGBoost model meant something.
AI engineer vs machine learning engineer: who to hire
An AI engineer wires up models that already exist. A machine learning engineer builds the model. Which one you hire decides whether you ship in two weeks or spend six months in a sandbox, and here is how I tell the two jobs apart.
What the WordPress Abilities API changes for AI in 6.9
I once spent three weeks bolting a permissions layer onto custom REST endpoints so an AI agent could not wreck a client’s product catalog. The Abilities API landing in WordPress 6.9 is the standard that job was missing.
WordPress AI architecture: plugin or Composer library?
A real question from WordPress Core AI work: should a shared AI adapter ship as a canonical plugin or a Composer library, and why that architecture choice shapes maintenance later.