Claude: The Strategic Analyst
Leaves 42 comments on your prompt design, all correct, all politely phrased, none skippable.
👋 I shared a prompt template built using the six building blocks of effective prompt design, then asked four top AI models to provide feedback. Each one had strong (and different) opinions.
Keep reading for Claude’s feedback, or jump to your favorite model’s feedback below:
Claude, the flagship model from Anthropic, approached the prompt like a staff engineer reviewing code. It wasn’t interested in formatting tweaks or specific word choices. Instead, it focused on where the prompt might break down, pointing out failure modes, edge cases, and long-term ambiguity. Where ChatGPT might say “this works for your use case,” Claude says “this is where it stops working at scale.”
Analyst Mode Activated
Claude immediately locked onto the clear structure and solid fallback logic. But what stood out were the gaps, the edge cases and unspoken assumptions I hadn’t fully addressed.
Take the assistant’s role: I described it as a “personal shopping assistant,” but Claude pushed for more specificity. Are we helping the user make a purchase? Or offering an unbiased summary of the reviews? That distinction affects tone, trust, and phrasing.
The quote section in the answer also worked on paper, but Claude flagged its limitations. What if multiple reviews apply? What if none are conclusive? Without clear guidance, the model could default to inconsistent behavior.
💡 Claude was the only model to critique the quality of the review data provided.
It argued that the sample reviews didn’t actually support the type of nuanced synthesis the prompt was aiming for. That’s a valuable insight, and one that looks beyond prompt structure to question the raw materials feeding the model.
Claude’s suggestions aren’t simple tweaks to existing syntax. These are design limitation for the system, and highlight practical ways this prompt would fail in production.
Claude’s Strategic Priorities
Claude doesn’t just follow a prompt. It evaluates the system behind it, scanning for ambiguity, failure modes, and missing logic. Its feedback aims to improve resilience and reduce risk, not just polish the surface.
That mindset reflects more than model tuning. It echoes Anthropic’s mission: to build aligned, interpretable AI that behaves safely and predictably. Claude’s feedback style mirrors the company’s enterprise-first focus, where clarity and control matter more than charm.
Claude’s perspective is strategic because Anthropic’s mission demands it. In risk-sensitive settings, vague instructions and inconsistent behavior are unacceptable. This is a model built to catch problems before they happen.
Takeaways for Builders
Claude is the kind of model you want reviewing your system prompts, not just one-off completions. It sees prompts as contracts between the user, the model, and the data. And it expects those contracts to hold under stress.
Here’s how to apply its feedback:
Match your prompt to your data
If users ask about travel and your reviews don’t mention it, instruct the model to acknowledge the gap instead of guessing.
Push for synthesis, not summary
Add guidance like: “Identify patterns or shared opinions across reviews, not just single quotes.”
Plan for disagreement
Include fallback logic like: “If reviews conflict, present both views and explain the uncertainty.”
Define the assistant’s stance
Be explicit: “Should the assistant recommend a product, remain neutral, or just report what others said?”
👋 I shared a prompt template built using the six building blocks of effective prompt design, then asked four top AI models to provide feedback. Each one had strong (and different) opinions.