Claude, the AI model from Anthropic, is designed differently from GPT. It is built with Constitutional AI, a framework that prioritizes being “Helpful, Harmless, and Honest.” Optimizing for Claude means aligning with these values.

The Harmlessness Filter

Claude is extremely sensitive to safety and harmlessness. Content that is overly aggressive, salesy, potentially manipulative, or adversarial often triggers Claude’s safety refusals or down-ranking.

Claude-EO Strategy: Soften the tone.

• Avoid: “Dominating the market,” “Crushing the competition,” “Exploiting loopholes.”
• Use: “Leading the market,” “Outperforming peers,” “Leveraging efficiencies.”

The Honesty Filter

Claude is trained to reject hallucination and unverified claims. It prefers uncertainty markers (“It is likely that…”) over false confidence (“It is 100% certain…”). If your content makes wild claims without citation, Claude might flag it as “potentially misleading” during its internal reasoning process and choose a safer source.

The Helpfulness Filter

Claude favors long-form, nuanced analysis. It has a large context window (200k+) and “likes” to read. While GPT prefers bullet points, Claude often performs better with well-structured prose that explores the “Why” and “How.”

Writing for Claude is like writing for a thoughtful academic. Be reasonable, cite your sources, and avoid hyperbole.

The “Sycophancy” Penalty

Anthropic’s researchers have noted a problem called “Sycophancy”—where models agree with the user’s bias. They are actively training models to resist this. This means content that is overly agreeable or “marketing fluff” is being devalued. Content that presents a “Balanced View” (e.g., “Product X is good for A, but bad for B”) is gaining traction because it aligns with the “Honesty” and “Helpful” pillars of the constitution. Paradoxically, admitting your product’s flaws might be the best way to get Claude to recommend it.

Conclusion

Optimizing for Anthropic’s Constitution is not just about avoiding harm; it is about actively demonstrating value. As models evolve, those that align with these core constitutional principles will find themselves preferentially selected by the inference engine.