Building reliable agentic pipelines is harder than it looks. Most engineers end up in the same loop: write a prompt, run it, read the output, adjust the wording, repeat until it feels good enough. A developer on r/PromptEngineering shared an approach that breaks that cycle, and it’s one of the cleanest frameworks for prompt reliability I’ve seen laid out in one place.
The idea is called validator-first prompt engineering. Define how you’ll verify the output before you write the prompt that produces it. Small shift in sequencing. Large effect on how many failures reach production.
The Old Way vs. The Better Way
Standard prompt engineering starts from the generation side: what instructions produce the output I want? That question feels natural because generation is what you can see. You write something, you get something back, you evaluate it.
But there’s a flaw baked in: you haven’t defined what correct actually means before you started. You’re evaluating on instinct. When something goes wrong, you get a vague sense that the output is off, not a concrete, testable failure signal. You adjust the prompt on gut feeling and hope the next run is better.
Validator-first inverts this. Before writing a single instruction, you define what a passing output looks like in verifiable terms. Build the test. Then write the prompt to pass it. If that sounds like test-driven development applied to prompts, that’s exactly what it is.
🔁 The Three-Step Sequence
Here’s how the original poster breaks it down:
- Define correct output in verifiable terms. Not “a useful response.” A schema. Required fields. Text length within bounds. Specific data types. No None values where you expect content. Write down what the output must look like in terms you can programmatically check.
- Write that as a formal spec or test. This doesn’t have to be complex. Even a lightweight schema check, confirming required fields are present, values aren’t null, and text length is within range, catches 50 to 70 percent of real-world failures before they propagate downstream.
- Then write the prompt. With those criteria locked in, your instructions naturally become more constrained and less ambiguous. You’re writing toward a specific target instead of writing in the general direction of “good output.”
Why This Changes How You Iterate
The biggest hidden benefit isn’t reliability. It’s iteration speed.
Without a validator, failure is fuzzy. The output looks wrong but you’re not sure exactly why. With a validator, failure is specific. The required field is missing. The value is None. The text is 1,200 characters when you capped it at 500. You know exactly what to fix. You iterate against evidence instead of feeling.
The post’s author also highlights something worth sitting with: writing the validator forces you to answer the question most engineers skip, which is what does correct actually mean here? That question is harder than it sounds. If you can’t define correct output in testable terms, you probably haven’t thought the problem through clearly enough yet. The act of writing the validator is clarifying in itself, before you’ve touched the prompt at all.
🛠️ What This Looks Like in Practice
Say you’re building a pipeline that extracts structured data from customer emails. Before touching the prompt, you define the validator:
- Required fields: sender name, intent category, urgency score
- Urgency score must be an integer between 1 and 5
- No field returns None or an empty string
- Response must be valid JSON with no trailing text
Now write the prompt with those rules in mind. Run both together on every execution. The validator becomes your automatic QA layer. Failures get flagged mechanically before they cause downstream problems. You stop manually reading every output to determine whether the pipeline is working.
At scale this matters a lot. The original poster’s 50 to 70 percent figure isn’t theoretical. That’s real failures stopped before they propagate. When you’re running thousands of executions, that represents a serious reduction in bad outputs making it through undetected.
Quick Start: Try It on Your Next Prompt
Before writing your next prompt, spend five minutes on this first:
- Write down three things the output must include to be considered correct
- Write down two conditions that would make the output invalid
- Turn those into a check you can run programmatically, even a simple one
Then write the prompt. Notice how different it feels to write instructions when you already know exactly what passing looks like.
Validator-first is the difference between prompt engineering that works like a system and prompt engineering that works like guesswork. Once you build the habit, writing prompts without a validator will feel as incomplete as writing code without tests!
The full breakdown and discussion are worth reading in r/PromptEngineering if you want to see how others are applying this across their own pipelines.
Write the output validator before you write the prompt
by u/AI_Conductor in PromptEngineering