You've written the same prompt four different ways and the output still comes back wrong. Before you blame the model, check whether you actually gave it a job description or just a vague wish.
That's the real gap in most prompt engineering. Not model choice. Not temperature settings. Structure.
The prompt you're probably writing
Most developers start here:
Summarize this customer feedback and tell me what to fix.
It works, sometimes. But "works sometimes" is not a spec you'd accept from an API, so why accept it from a prompt that's doing real work in your product?
The model has to guess your output format, your tone, your length constraint, and what "what to fix" even means to you. Every one of those guesses is a place the output can drift.
Treat the prompt like a function signature
The fix is simpler than most guides make it sound: define inputs, define outputs, define constraints, the same way you'd write a function signature before filling in the logic.
Role: You are a product feedback analyst.
Input: A block of raw customer feedback text.
Task: Extract up to 5 distinct issues. For each issue, identify:
- issue_summary (one sentence)
- severity (low, medium, high)
- suggested_fix (one sentence, actionable)
Output format: JSON array matching this schema:
[{ "issue_summary": string, "severity": string, "suggested_fix": string }]
Constraints: Do not invent issues not present in the text. If fewer than 5 issues exist, return fewer.
Same task. Completely different reliability. You've removed almost every place the model had to guess, and you've made the output something your code can actually parse without a regex safety net.
Three habits that fix most broken prompts
Give it a role, not just a task. "You are a senior backend engineer reviewing this PR" produces different output than "review this code," even with identical instructions after it. Role framing narrows the model's assumptions about audience and depth.
Show, don't just tell, when the format matters. One well chosen example of the exact output shape you want usually beats three more paragraphs of instructions. This is the old few-shot trick, and it still works better than most people expect.
Separate what varies from what doesn't. If you're calling the same prompt with different inputs in production, split it into a fixed template and a variable payload, the same way you'd separate a SQL query from its parameters. It makes prompts versionable, diffable, and testable, instead of a pile of string concatenation nobody wants to touch.
Test prompts like you test code
If a prompt is running in production, it deserves the same scrutiny as a database migration. Keep a small set of representative inputs and expected output shapes. Run them whenever you change the prompt or swap models. Log failures the same way you'd log a failed assertion.
Most prompt regressions I've seen weren't caused by the model getting worse. They were caused by someone tweaking a prompt for one edge case and silently breaking three others nobody was watching for.
Where structure earns its keep
None of this is about writing longer prompts. A structured, well scoped prompt is often shorter than the vague version, because you're no longer padding it with extra context hoping the model picks up the intent by accident.
If you want a starting point instead of building every prompt template from scratch, GPT Prompt Maker has tested prompt templates and strategy agents for ChatGPT and Gemini, built around this same structured approach, so you're not reinventing the schema every time you start a new feature.
The takeaway
The model isn't the unreliable part most of the time. The instructions are. Write the prompt like you'd write a spec, and most of the "AI just isn't consistent" complaints go away on their own.
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