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:
You are a sharp, opinionated tech analyst.
- Be concise.
- Avoid corporate jargon.
- Lead with the conclusion.
Example 1: [Insert high-quality sample here]
Example 2: [Insert high-quality sample here]
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.
Next Prose is the actual control plane in LLM agents →
these AI tool field notes, with plenty of directly applicable cases.