# 56 > Source: > Published: 2026-08-14 21:53:15+00:00 # 56 ## 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. [Next Prose is the actual control plane in LLM agents →](/en/threads/6303/) [these AI tool field notes](https://tanyan888.com/), with plenty of directly applicable cases.