There is nothing more frustrating than building a beautiful flow or a sleek backend, only to have a Large Language Model (LLM) decide that today is the day it wants to be a “poet” instead of a data processor.
We’ve all been there: your API expects a strict JSON object, but the model decides to preface it with, “Certainly! Here is the data you requested in JSON format:” — and suddenly, your entire production pipeline crashes with a SyntaxError.
Over the last year, I’ve moved away from “prompt engineering” alone and started focusing on AI hacks. By manipulating the actual request object, you can force the model into submission.
Here is how I keep my LLMs on a short, technical leash.
1. The “Golden Rule”: response_format
If you are still begging the AI to “strictly reply with JSON” in the prompt text, you’re working too hard. Modern models (like GPT-4o or Gemini) now support a dedicated parameter in the request body.
By setting response_format: { "type": "json_object" }, you are essentially telling the model’s brain that its only possible output vocabulary is valid JSON.
The Hack: In your JS Function, don’t just send the prompt. Send this:
payload = {
"model": "gpt-4o",
"messages": [...],
"response_format": { "type": "json_object" }
};
Note: You must still include the word “JSON” somewhere in your system or user message for this to work.
2. Token Limitation: max_tokens
We often think of max_tokens as a way to save money, but I use it as a logical constraint.
If I am asking the model for a “Yes” or “No” classification, I set max_tokens to 1. This physically prevents the model from explaining why it chose “Yes.” It has to give me the character and shut up.
3. The “Creativity Killswitch”: temperature and top_p
For developers, temperature: 1.0 is the enemy. It introduces randomness. I want the same input to produce a similar, high-quality output every time.
temperature: 0: Makes the model deterministic. It will always choose the most likely next token.top_p: 0.1: This is “Nucleus Sampling.” Setting it low ensures the model only considers the top 10% of most likely words.
4. logit_bias: Banning or Forcing Words
This is the ultimate “under-the-hood” hack. Every word (token) has an ID. With logit_bias, you can manually increase or decrease the probability of specific tokens appearing.
- Want to ban the word “Apologies”? Find its token ID and set the bias to
-100. - Want to force a specific ID? Set it to
+100.
This is incredibly useful if you have a UI that strictly only accepts three specific command strings and you want to ensure the AI never hallucinates a fourth one.
5. seed: For Reproducible Debugging
One of the hardest parts of AI development is “flaky tests”, where a bug happens once but you can’t recreate it because the AI changed its mind.
By passing a seed: 12345 in your payload, the model will attempt to generate the exact same response for the same prompt. This is a lifesaver during the debugging phase.
Summary
Prompt engineering is for writers; Payload Engineering is for developers. By shifting your control logic from the text to the parameters, you build systems that are resilient, predictable, and—most importantly—ready for production.
Stop asking nicely. Start configuring.