I was knee-deep in a custom WooCommerce inventory sync for a client last month. Massive catalog—nearly 50k SKUs—and we had several custom database tables for logging and delta tracking. I fired up a new AI coding agent to write a cleanup script for the sync logs. It was a total mess. The agent started hallucinating table names like wp_sync_records when the actual table was wp_bbioon_inventory_delta. Without the right context, the agent was basically a blindfolded intern with a chainsaw. This is why mastering Effective AI Programming isn’t just about the prompt; it’s about the data you feed the beast.
My first instinct was just to give the agent SSH access and let it “explore” the codebase. That was a rookie move. It wasted thousands of tokens listing directories, hit rate limits, and honestly, it still didn’t understand the why behind our business logic—like why we only purge logs if they aren’t flagged as ‘critical’. Trust me on this: an agent without a map will always take the most expensive route to the wrong destination. You’re much better off using a WordPress MCP adapter to bridge that gap.
The Markdown Schema Hack for Effective AI Programming
One of the fastest ways to fix this is context engineering. Instead of letting the AI guess your infrastructure, you provide a single, compressed source of truth. I started keeping an INFRA.md file in the root of my projects. This file contains the schema for custom tables, S3 bucket prefixes, and even production log locations. It turns the AI from a guesser into a precise tool. This is a core pillar of effective context engineering that most devs skip.
/**
* A helper script to generate a context-rich Markdown file
* for your AI coding agent to read.
*/
function bbioon_generate_ai_context_file() {
$tables = [
'bbioon_inventory_delta',
'bbioon_sync_logs'
];
$output = "# Project Database Schema\n\n";
foreach ( $tables as $table ) {
global $wpdb;
$full_table = $wpdb->prefix . $table;
$columns = $wpdb->get_results( "DESCRIBE $full_table" );
$output .= "## Table: $full_table\n";
foreach ( $columns as $column ) {
$output .= "- {$column->Field} ({$column->Type})\n";
}
$output .= "\n";
}
file_put_contents( ABSPATH . 'AGENTS.md', $output );
}Whenever you notice your agent struggling—listing tables over and over or reasoning about which file to open—it’s a context red flag. Don’t let it loop. Stop the generation, update your AGENTS.md or PROJECT.md, and tell the agent to re-index. It saves tokens and, more importantly, it saves your sanity. If you’re tired of “vibe coding” your way through errors, you might want to read my guide on why you should stop vibe coding your next WordPress project.
Bridging the Human-AI Context Gap
We often forget that half of our technical decisions happen in meetings or casual Slack chats. The AI isn’t in that huddle. If you discussed in a meeting that the summarization feature only needs to run for external attendees, the AI will never know unless you write it down. I’ve started using AI tools to summarize my own technical shaping meetings and dropping those summaries directly into the project’s context window. Using the Model Context Protocol (MCP) to link tools like Linear directly to your agent is a game-changer for this.
Look, this stuff gets complicated fast. AI agents are incredibly powerful, but they are only as good as the context you provide. If you’re tired of debugging someone else’s mess and just want your site to work with modern, AI-assisted efficiency, drop me a line. I’ve probably seen it before. For more on the future of this tech, check out Towards Data Science’s deep dive into agent facilitation.
Key Takeaways for Senior Devs
- Document everything technical: If it isn’t in a file, the AI doesn’t know it.
- Use centralized schema files: Stop the agent from “exploring” and wasting tokens.
- Summarize oral discussions: Bring the meeting context into the codebase via Markdown.
- Pattern recognition: If the agent asks the same question twice, the context is missing.
Think of your agent as a junior developer who has a perfect memory but zero intuition. You wouldn’t throw a junior into a 50k SKU WooCommerce site without a map, right? Don’t do it to your AI.
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