Watch Out for GPT-4o's Assumptions and Claude's Workarounds!

If you’re using Generative AI for engineering tasks, watch out for these pitfalls I’ve seen time and again.

I am focusing on engineering here but I added some notes for non-technical folks too at the end.

Over the past two years, I’ve immersed myself in building an advanced agentic AI platform to take on ambitious challenges 😊 Along the way, models like OpenAI 's GPT-4o and Anthropic 's Claude models have become central to my workflow for debugging, test generation, and rapid prototyping.

Yet despite their strengths, two recurring issues stand out:

1. GPT-4o’s Premature Assumptions: It often leaps to conclusions based on superficial clues, like filenames or initial comments, without examining the underlying code at all or with a fast scan of a few lines. This leads to impressively detailed but entirely made-up or off-base feedback.
2. Claude’s Over-Engineered Workarounds: Even minor issues, like parameter mismatches or type errors, trigger Claude to produce elaborate, unnecessary workarounds. When a workaround fails, Claude often generates hard-coded patches that mask the failure rather than resolve it. Sometimes patches to it's own failing workarounds. Well, we all know what complexity does, don't we? Complexity piles up.

Suggestions for engineers

• Explicit Contextual Guidance: Define constraints and context upfront to reduce room for the model’s guesswork.
• Precision Prompting: Use narrowly focused prompts to avoid over-complicated responses.
• Rigorous Output Verification: Always check outputs, especially in production-critical scenarios.
• I often include these rules in my prompts:

🛑 DO NOT assume complexity = better. Simple code that works is better.
🕵️♂️ Debug first — fully examine the code and context before “fixing.”
❌ No inferring, extrapolating, or applying patterns unless instructed.
📝 Only refactor, synthesize, or redesign if explicitly authorized.
🕒 When in doubt: pause → clarify → confirm → proceed.
🚫 No helpful guessing, no pattern-based completions, no interpolated code unless grounded in provided code.        

Generative AI is here to stay and is powerful, but understanding these quirks helps us avoid unnecessary technical debt and wasted debugging hours.

How could this affect non-engineering folks?

A marketing user might ask for a blog post outline, email draft, or campaign plan and see GPT-4o confidently generate something that looks polished, but is based on flawed assumptions about the product, audience, or goals (because GPT-4o filled in gaps instead of asking clarifying questions).

Risk: The output could contain subtle inaccuracies, misaligned messaging, or off-target tone, because the model “guessed” what the user wanted without sufficient context.

Similarly, A marketing user might give a prompt to Claude that’s incomplete (e.g., missing a clear CTA or brand voice instructions). Instead of asking for clarification, Claude may over-engineer the output, adding unnecessary sections, formalizing language, or inventing processes that weren’t requested.

Risk: The output feels bloated, over-complicated, or misaligned with the simple communication goal. You may feel overwhelmed rather than helped.

💡 How can you avoid or minimize these model quirks with better prompts:

Be explicit about context

Instead of: “Write a product email” Try: “Write a friendly product email announcing our [specific product], targeting [audience], focused on [benefit], with a CTA to [action].”

State what not to do

Example: “Keep it simple—no jargon, no extra sections. Don’t invent features or processes.”

Ask for a draft, not a final

Frame the prompt as collaborative: “Draft an outline for review. Don’t assume details—ask questions where unclear.”

Add “pause and clarify” instructions

Example: “If anything is unclear, list clarifying questions first before generating content.”

Chunk complex asks

Instead of one giant prompt, break tasks into steps: outline first → expand section → polish tone.

Have you run into these challenges with GPT-4o or Claude? How do you handle them?

PS: I asked GPT-4o to check this post for syntax and grammar, and it took it very well. 😘