Building a Self-Updating Writing Style Guide for AI-Assisted Content

A team developing the Noren voice extraction platform has created a dynamic writing style guide that evolves through actual use rather than being written once and ignored. Their 117-line Markdown guide has been edited six times in one week as they publish content across blog, LinkedIn, and X platforms.
How the Guide Works
Every piece of content serves as a test case for the guide. If the writing sounds like AI, the guide has failed—not the writer. The guide is stored in CLAUDE.md so Claude reads it at the start of every writing session and keeps it in memory for each task. Claude follows the rules, then the team audits the output against the guide.
Key Rules and Practices
Start by writing, not by writing rules: They wrote their first post, reviewed the output, and documented what worked and what didn't. Rules extracted from real writing are 10x more useful than rules invented in a vacuum.
Banned words keep growing: Started with obvious AI-sounding words like "game-changing," "leverage," "optimize," and "revolutionary." Recently added "cadence" because it kept appearing in every AI draft reviewed. If a word feels like it belongs in a ChatGPT output, it goes on the list. They're at 25 banned words now, and nothing comes off.
Track AI tells as a checklist: Instead of vague advice like "avoid AI-sounding writing," they identify specific patterns with specific fixes:
- Triple parallel structures (three identical sentence frames in a row)
- Summary sentences after examples that already proved the point
- Hedge qualifiers nobody says out loud ("it's worth noting that," "even if they can't articulate why")
- Gerund fragment litanies (stacked -ing fragments that add word count, not meaning)
Each pattern gets a rule with a concrete example of what to do instead.
Rule Evolution Through Use
"Flow over chop" correction: Their guide initially said to use short sentences for impact, but they realized they were using them everywhere—every section ended with a two-word fragment. New rule: commas and connective words are the default, and short sentences have to earn their pause.
Example repetition: They used "semicolons" as their go-to example three times in one post across different sections. Readers notice even if you don't. Now every example appears only once.
Iterative Improvement Process
The team updates the guide after every piece published. Each post either confirms the guide works or exposes a gap. The guide is wrong about something every week, and they fix it every week. When Claude breaks a rule, it indicates the rule needs to be clearer—not a stronger warning. If Claude keeps producing triple parallel structures, the rule needs a better example.
After 10 blogs/longform pieces and 20-40 short form pieces, they plan to run the guide through Noren voice extraction to create an actual voice guide for the brand.
📖 Read the full source: r/ClaudeAI
👀 See Also

Cognitive Science Technique Boosts LLM Creativity: /reframe Slash Command for Claude Code
A Reddit user developed a /reframe slash command for Claude Code that implements a cognitive science technique called distance-engagement oscillation, which improved creative problem-solving by 40% in tests across three open-weight LLMs.

TigrimOS v1.1.0 and Tiger CoWork v0.5.0 released with remote agent swarms and configurable governance
TigrimOS v1.1.0 and Tiger CoWork v0.5.0 released today add swarm-to-swarm communication between remote instances and five configurable governance protocols. Both are self-hosted, free, and open source.

Gemma 4 26B vs Qwen 3.5 27B: Local Business Workflow Benchmark on RTX 4090
A developer tested Gemma 4 26B and Qwen 3.5 27B on an RTX 4090 workstation for 18 real business operator tasks. Gemma won 13-5, showing faster speed and better discipline for daily execution work, while Qwen excelled at broader strategic thinking.

Multi-Agent Career Mentor Built with Ollama and MCP for Local AI
A developer built a 5-agent AI system that analyzes resumes and generates career intelligence reports using Ollama with llama3 locally. The system chains agent outputs so each builds on previous context, with MCP handling tool integration.