We need to talk about how most developers are currently using LLMs. Most of you are treating ChatGPT or Claude like a magic 8-ball—throwing in a vague sentence and hoping the output doesn’t break the production site. This “Zero-Shot” approach is why your code is riddled with hallucinations and legacy functions. To truly hit that 5x performance boost, you need to master Few-Shot Prompting.
I’ve spent 14 years wrestling with WordPress core, and if there’s one thing I’ve learned, it’s that context is everything. Whether you are refactoring a complex WooCommerce checkout or debugging a race condition in a custom transient, the LLM needs to see your “intent” through actual implementation examples, not just natural language descriptions.
The Problem with Zero-Shot Prompting
In contrast to a refined workflow, a Zero-Shot prompt looks like this: “Write a function to add a custom order status to WooCommerce.” Consequently, the AI might give you code that misses the wc- prefix requirements or fails to register the status in the bulk actions dropdown. It’s technically “correct” but architecturally a disaster.
Furthermore, without examples, the AI has to guess your coding standards. Does your agency use a specific namespace? Do you prefix functions with bbioon_? Without Few-Shot Prompting, you’re going to spend more time refactoring the AI’s “help” than you would have spent writing the code from scratch.
Few-Shot Prompting in Action: The Senior Approach
The secret is providing 2–3 “shots” (examples) of how you’ve solved similar problems before. Specifically, if I’m building a new REST API endpoint, I’ll feed the agent a working endpoint from a previous project. This eliminates ambiguity and ensures the agent follows my exact security patterns (like proper permission_callback checks).
I recently wrote about this in my guide on stopping AI hallucinations with better context. By providing a reference, you are essentially training the model on the fly for your specific codebase.
A Practical Example: Custom WooCommerce Logic
Let’s look at the difference. Below is the “Naive” approach vs the “Few-Shot” result for a custom status registration.
// The Naive Approach (Zero-Shot) - Generic and often buggy
function register_my_status() {
register_post_status( 'shipped-to-hub', array(
'label' => 'Shipped to Hub',
'public' => true
) );
}
add_action( 'init', 'register_my_status' );Now, notice how much cleaner and “production-ready” the code becomes when you provide an existing status registration as a Few-Shot example. The AI learns to include the wc- prefix and the proper array parameters for the WooCommerce ecosystem.
// The Senior Approach (Few-Shot Result)
// This follows the exact pattern provided in the prompt examples
function bbioon_register_hub_order_status() {
register_post_status( 'wc-shipped-hub', array(
'label' => _x( 'Shipped to Hub', 'Order status', 'bbioon' ),
'public' => true,
'exclude_from_search' => false,
'show_in_admin_all_list' => true,
'show_in_admin_status_list' => true,
'label_count' => _n_noop( 'Shipped to Hub <span class="count">(%s)</span>', 'Shipped to Hub <span class="count">(%s)</span>', 'bbioon' )
) );
}
add_action( 'init', 'bbioon_register_hub_order_status' );Organizing Your Work for LLM Context
To leverage Few-Shot Prompting effectively, your local environment needs to be organized. Therefore, I maintain a library of “Pattern Folders.” If I’m working on a CLI tool, I point my agent to my /patterns/wp-cli/ directory. If I’m doing marketing material, I show it my previous LinkedIn carousels.
According to the Prompt Engineering Guide, providing even just one or two examples can drastically improve output quality. In my own workflow, this has turned 2-hour debugging sessions into 15-minute refactors. For a deep dive into my setup, check out my terminal-first AI coding setup.
Look, if this Few-Shot Prompting stuff is eating up your dev hours, let me handle it. I’ve been wrestling with WordPress since the 4.x days.
The Takeaway
Stop asking the AI to “think.” Start asking it to “replicate.” By feeding it high-quality examples of your previous work, you eliminate the guesswork. The more work you ship, the bigger your library of “shots” becomes, and the faster you’ll code. It’s a flywheel effect that separates the senior architects from the script kiddies. Ship it.
