Study reveals AI models have distinct “coding personalities” affecting quality

AI coding expert Lance Eliot warns that successful “vibe coding”—using natural language prompts to generate code through AI—requires understanding the distinct coding personalities of different large language models. A recent study analyzing popular LLMs like GPT-4o, Claude Sonnet, and Llama revealed that each AI has unique coding styles, from “Senior Architect” to “Rapid Prototyper,” which directly impact the quality and security of generated code.

What you should know: Vibe coding allows non-technical users to create applications by describing what they want in plain English, with AI handling the actual programming.

• The approach has democratized app development, theoretically enabling anyone to become a “vibe coder” without traditional programming skills.
• However, current AI-generated code often contains security vulnerabilities, lacks proper error checking, and may include technical debt that professional developers would avoid.
• Most vibe coders remain unaware that their AI-generated applications might contain “ticking timebombs” of bugs and hackable vulnerabilities.

The coding personality breakdown: Research from Sonar identified distinct coding styles among leading LLMs, each with specific strengths and weaknesses.

• GPT-5-minimal: Labeled as “Baseline Performer”
• Claude Sonnet 4: Characterized as “Senior Architect”
• Claude 3.7 Sonnet: Described as “Balanced Predecessor”
• GPT-4o: Categorized as “Efficient Generalist”
• Llama 3.2 90B: Termed “Unfilled Promise”
• OpenCoder-8b: Identified as “Rapid Prototyper”

Why this matters: Understanding these AI coding personalities helps developers choose the right tool for their specific needs and anticipate potential issues.

• Just as human programmers have distinct coding styles—from precise and readable to messy and confusing—AI models exhibit consistent patterns in how they generate code.
• The study found that while all tested LLMs can generate syntactically correct code and solve complex problems, they share “blind spots” including “a consistent inability to write secure code, a struggle with engineering discipline, and an inherent bias towards generating technical debt.”

The human element remains crucial: Despite AI’s coding capabilities, human oversight is still essential for quality assurance.

• AI-generated code often requires human developers to review, debug, and strengthen the output before deployment.
• Eliot shared an example from his software engineering career involving a contractor who deliberately used cryptic variable names like “Beethoven” and “Mozart” instead of descriptive terms, making code nearly impossible to understand.
• This illustrates how coding style—whether human or AI-generated—significantly impacts maintainability and comprehension.

Pro tips for better vibe coding: Experienced users can improve AI-generated code quality through strategic prompting techniques.

• Instead of generic requests, specify exactly what type of code quality and style you want the AI to generate.
• Be explicit about requirements for error checking, security measures, and code readability.
• While not a “silver bullet,” detailed prompting can notably improve the chances of receiving more professional-quality code.

What’s next: The rapid evolution of AI coding capabilities means these personality profiles may shift as models are updated or customized for specific coding tasks.

• AI makers are increasingly focusing resources on improving code generation quality, recognizing the significant market demand.
• Future developments may automatically include robust error checking and security measures, bringing AI-generated code closer to professional developer standards.
• Eliot promises upcoming guidance on effective prompting strategies for AI code generation.