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The anatomy of a bloated prompt
If you are doing a deep dive into prompt engineering, you'll recognize this pattern. My prompt eventually became a graveyard of:
- Banned phrases: A long list of specific words the AI wasn't allowed to use.
- Structure bans: Rules against specific sentence shapes that felt "too AI."
- Opener restrictions: A massive list of forbidden ways to start a paragraph.
- Register rules: Complex instructions on formality levels for different content types.
- Meta-rules: Instructions telling the AI which rule to follow when two other rules conflicted.
The moment you need "rules for your rules," you've stopped writing a prompt and started writing a buggy program that no one is actually debugging.
Over-engineering with validators
Because prompt rules are requests rather than hard guarantees, I started adding post-generation validators in the code. I built a stack of regex checks to catch banned shapes, vocabulary blocklists, and rejection passes. At one point, I had seven different validators running in a bundle.
I thought I was being rigorous, but I was actually just compounding patches to compensate for the fact that the model was being steered into a corner.
The "Committee" Effect
The result wasn't a perfect voice; it was a sterile one. The output became flat and cautious. It sounded like it had been written by a corporate committee trying to avoid any possible mistake rather than a human with a point of view.
For a long time, I blamed the LLM. I actually started preparing a budget to move to a more expensive, "smarter" model tier, thinking that would solve the quality dip. I was wrong. A more powerful model given 56,000 tokens of contradictory restrictions would have just been a more expensive version of the same bland output.
If you're building an AI workflow, stop adding "don't do X" rules. Instead, focus on a few high-quality worked examples. Here is a simplified version of how I shifted from a rule-heavy prompt to an example-driven one:
# Role
You are a sharp, opinionated tech analyst.
# Voice Guidelines
- Be concise.
- Avoid corporate jargon.
- Lead with the conclusion.
# Examples of Target Voice
Example 1: [Insert high-quality sample here]
Example 2: [Insert high-quality sample here]
# Task
Rewrite the following input to match the style of the examples above.Cutting the bloat usually does more for the output quality than upgrading the model ever will.