Claude for Design Work: How to Stop Repeating the Same Taste Arguments Every Session
Running a web agency on Claude (Cursor, Claude Code, custom skills) produces solid code fast, but design taste degrades across sessions. The root cause: Claude has no persistent memory of rejected variants, accepted tokens, or design judgments. Brand facts are preserved; taste history is not.
The Core Problem
- A Stripe-inspired hero brief (brand docs, screenshots, 6K-token system prompt) generated gradient mesh, glassmorphic cards, feature pills, glow CTAs — generic 2023 SaaS #4 output. It took 18 iterations to fix, and spacing tweaks in prompt 13 reintroduced the glassmorphic card killed in prompt 5.
- A premium law firm brand kit produced fintech-style rounded cards and soft blue because Claude's training distribution averages “professional + clean” to that look.
- Two sections from the same kit (hero vs testimonials) in fresh sessions produced mismatched type pairings and button styling — as if designed by different studios.
Why Prompts and Projects Don't Fix It
Prompts forget. CLAUDE.md stores facts, not judgments. Projects pin documents, not taste history. The accumulated decisions (“8px radius forever,” “no glassmorphism on this account”) vanish each session.
The Workaround (Custom Memory Layer)
The author wired a separate layer that holds:
- Rejected variants
- Accepted tokens
- Voice rules with examples of what the client actually shipped
This subset is injected into every Claude call as live context. Same model, but now reading taste history instead of guessing.
Open Questions
The author asks the community:
- How do you keep taste consistent across sessions in AI-driven design work?
- Has anyone gotten Projects +
CLAUDE.mdto persist judgments (not just facts)? - Anyone using a memory layer (custom or off the shelf) specifically for design work?
📖 Read the full source: r/ClaudeAI
👀 See Also
AGENTS.md Schema for LLM-Compiled Knowledge Bases with Learning Layer
AGENTS.md v1.0 provides a schema standard for Claude to build and maintain personal research wikis from raw sources, including a spaced repetition learning layer with automatic flashcard generation and knowledge gap tracking.
AlterSpec v1.0: Runtime Policy Enforcement for AI Agents
AlterSpec v1.0 is an open-source runtime enforcement engine that sits between AI agents and their tools, evaluating actions against YAML-defined policies before execution. It provides allow/deny/review decisions, cryptographic policy signing, and audit logging.
Recursive Self-Improvement Framework for AI Coding Agents Using Claude Code
An open-source framework enables AI coding agents to recursively improve themselves using Claude Code. The system analyzes agent traces, identifies failure patterns, and implements fixes, achieving a 25% performance increase in one test cycle.
Two Patterns for Preventing AI Agent Memory Rot: AutoDream and Skeptical Retrieval
OpenClaw introduces two MIT-licensed patterns to address file-based AI memory rot: AutoDream for nightly memory consolidation and Skeptical Retrieval for decay-weighted memory scoring. Both work together in a self-improving loop to keep agent context current.