In a recent LinkedIn post, Itay Hacmon discusses a counterintuitive approach to optimizing AI models like Claude: reducing the complexity and length of instructions. Hacmon shares his findings and practical advice on how to streamline AI setups without sacrificing performance, drawing parallels to Anthropic’s own adjustments to Claude Code.
Hacmon highlights a significant shift in how AI models, particularly newer generations, process information. He points out that the extensive guardrails and detailed instructions once thought necessary are becoming less critical.
“The logic has been reversed: more instructions no longer equal more results. The rules you wrote in the past were for models that needed fencing. Models from generation 5 have much better judgment and simply need it less.”
The Paradox of Prompt Complexity
Itay Hacmon argues that the prevailing wisdom of providing lengthy and detailed prompts to AI models may be outdated. He explains that as AI models evolve, their inherent judgment and understanding improve, making excessive instructions potentially counterproductive. Hacmon’s own experience led him to discover that certain rules within his setup were hindering rather than helping.
Personal Discovery and Overhaul
Hacmon details his personal audit of his AI setup, uncovering several inefficiencies that were consuming valuable processing resources. He notes:
“I checked my setup last week and found a rule in CLAUDE.md that was actually hindering more than helping. And that was just the beginning:
→ Nine skills I didn’t touch, which burned 610 tokens every session
→ A hook that was supposed to run for a second but ran for five minutes, for months
→ Two broken folders that never loaded
→ A lag of five updates behind”
This experience led Hacmon to conclude that the very rules implemented to enhance AI reliability could, paradoxically, be diminishing it.
A Practical Guide to Prompt Optimization
To help others navigate this issue, Itay Hacmon has distilled his findings into a practical guide. He emphasizes the importance of identifying and removing unnecessary instructions that can bog down AI performance.
Key Takeaways from Hacmon’s Guide
According to Hacmon, the guide offers actionable steps for users to clean up their AI setups. It includes:
- Six transitions, with before-and-after examples from his own setup.
- A step-by-step walkthrough of his personal optimization process.
- Methods for transforming rigid rules into more adaptive, judgmental ones.
- Guidance on what to keep and what to remove from files like CLAUDE.md.
- A strategy for automatically detecting redundant rules.
Hacmon’s core message is that effective AI interaction is evolving. Instead of overwhelming models with instructions, the focus is shifting towards clearer, more concise prompts that leverage the AI’s advanced capabilities.
“The rules you added in the past to make the model more reliable – are precisely the reason it became less reliable.”
By sharing his experience and a structured approach, Itay Hacmon aims to empower users to optimize their AI interactions, leading to better performance and efficiency.
📝 About This Content
This article is based on insights shared by Itay Hacmon on LinkedIn.
📅 Originally posted on August 5, 2026 | View original post on LinkedIn →