AI's 'Overthinking' Problem: Pitfalls and Practical Solutions When Using Claude Code

• Target Audience: IT Engineers using AI assistants (Claude Code, GitHub Copilot, Cursor, etc.) in their work
• Prerequisites: Basic experience with AI coding tools
• Reading Time: 12 minutes

Overview

As system development using AI coding assistants like Claude Code and GitHub Copilot becomes widespread, fundamental weaknesses of these tools have been revealed by 2024-2025 research. This article explains the “overthinking” problem where AI “thinks too much” and reaches wrong conclusions, and the “comprehension-competence gap” where AI understands rules but fails to execute them correctly, presenting practical countermeasures developers should take.

Chain-of-Thought Reasoning and Its Limitations

What Is Chain-of-Thought (CoT)?

Chain-of-Thought (CoT) is a technique where AI explicitly generates a “chain of thought” when solving complex problems¹. For example, when solving math problems, rather than immediately producing an answer, showing the step-by-step reasoning process leads to more accurate answers.

Modern models like Claude, GPT-4, and Gemini utilize this CoT reasoning internally, and so-called “reasoning models” like DeepSeek-R1 and o1 are designed with this approach at their core.

Problem 1: Overthinking

However, 2025 research has identified fundamental problems with this CoT reasoning.

Amazon Science’s research team reports the “overthinking” phenomenon where reasoning models generate overly detailed and unnecessarily long reasoning steps². This problem manifests as follows:

Excessive reasoning on simple problems: For the simple comparison question “Which is larger, 0.9 or 0.11?”, reasoning models showed the following behavior³:

• QwQ-32B: 19 seconds to correct answer
• DeepSeek-R1: 42 seconds to correct answer

A problem a human could answer in 1 second, and AI spends enormous “thinking” on it.

Resource waste: According to IEEE Spectrum reports, reasoning models consume 7-10 times more tokens than non-reasoning models on simple tasks, resulting in significant cost increases to achieve the same accuracy³.

Ignoring correct hints: Even more serious, experiments confirmed cases where even when correct answers were explicitly injected into the reasoning process, models ignored those hints and continued with erroneous reasoning². This indicates models have a tendency to over-commit to their own reasoning paths.

Problem 2: Comprehension-Competence Gap

The paper “Comprehension Without Competence” published in July 2025⁴ points to another fundamental problem with LLMs.

The “knows but can’t do” phenomenon: LLMs can correctly verbalize (explain) principles yet fail to apply those principles in actual tasks. Researchers call this “computational split-brain syndrome,” analyzing that instruction understanding and action execution are functionally separated.

flowchart TB
    subgraph LLM["LLM Internals"]
        A["Understanding<br/>(Grasping Rules)"]
        B["Execution<br/>(Performing Tasks)"]
    end

    Input["Input"] --> A
    A -.->|"Separation"| B
    B --> Output["Output"]

    classDef gapStyle stroke:#d29922,stroke-width:3px,stroke-dasharray: 5 5
    linkStyle 1 stroke:#d29922,stroke-width:2px,stroke-dasharray: 5 5

Architectural limitations: Researchers conclude that while LLMs function as powerful “pattern completion engines,” they lack the architectural foundation for principled, compositional reasoning⁴. This consistently causes problems in tasks requiring mathematical operations, relational reasoning, and logical consistency.

Specific Problems Encountered in Practice

How do these research findings manifest in daily development work using tools like Claude Code?

Pattern 1: Overreaction to Simple Fixes

# Developer request
"Change this variable name from userId to userIdentifier"

# AI's excessive response (overthinking example)
- Change variable name
- Also change all related method names to "better naming"
- Add comments
- Extend type definitions
- Add related test cases
- Update documentation

# Expected response
- Change variable name (1-line change)

Pattern 2: Can Explain Rules but Can’t Apply Them

# Developer request
"This project uses single quotes as convention.
 Fix the code to follow this convention."

# AI's understanding (correct)
"I understand the single quote convention.
 All string literals should be enclosed in ' '"

# AI's output (comprehension-competence gap)
const message = "Hello, World";  // Still double quotes
const name = "user";             // Still double quotes

Pattern 3: Going Astray in Complex Reasoning

# Developer request
"Parse this API response and write code that
 handles errors appropriately"

# AI's problematic reasoning
1. First, let's comprehensively classify error types
2. Create custom exception classes for each error
3. Retry logic is probably needed too
4. Log output format should be unified
5. Behavior should be configurable via config files
6. ...(significantly deviating from original requirements)

Practical Countermeasures for Developers

Here are organized countermeasures developers can take for these problems.

Countermeasure 1: Clear Task Boundaries

When requesting from AI, explicitly state what to do and what not to do.

# Effective prompt example

## Do
- Add null check to getUserById function

## Don't
- Modify other functions
- Change naming conventions
- Add comments
- Refactor

Countermeasure 2: Incremental Task Splitting

Split complex tasks into small units so AI doesn’t “overthink.”

flowchart TD
    A["Large Task<br/>'Implement authentication system'"] --> B["Task Split"]
    B --> C1["1. User model definition"]
    B --> C2["2. Password hash function"]
    B --> C3["3. JWT token generation"]
    B --> C4["4. Auth middleware"]
    B --> C5["5. Endpoint implementation"]

    C1 --> D1["✅ Review/Confirm"]
    D1 --> C2
    C2 --> D2["✅ Review/Confirm"]
    D2 --> C3

    classDef taskStyle stroke:#2ea44f,stroke-width:2px
    class C1,C2,C3,C4,C5 taskStyle

Countermeasure 3: Immediate Output Verification

Don’t blindly trust AI output—always verify in areas prone to “comprehension-competence gap.”

Areas where verification is especially important:

• Arithmetic calculations/numerical processing
• Regular expression patterns
• Edge case handling
• Security-related logic
• Data type conversions

# Example: Auto-testing AI-generated code
npm test -- --coverage

# Type checking
npx tsc --noEmit

# Linter
npx eslint . --fix

Countermeasure 4: Using Custom Instructions

Explicitly state project-specific rules in Claude Code’s CLAUDE.md, Cursor’s .cursorrules, etc.

# CLAUDE.md example

## Coding Conventions
- Use single quotes for strings
- 2-space indentation
- Type annotations required

## Rules for Changes
- Only make requested changes
- Only suggest related code "improvements"
- Request confirmation for large changes

## Things to Avoid
- Excessive abstraction
- Adding unused utility functions
- Unrequested refactoring

Countermeasure 5: “Interrupting” and “Resuming” Reasoning

When AI begins lengthy reasoning, interrupt at appropriate times and redirect.

# When AI starts heading in an overly complex direction

User: Stop. Think more simply.
      The requirement is just "return default value when null."
      Implement with a single if statement.

Future Improvements: Directions from Research

Research communities are proposing the following improvement methods for current problems.

Mixture of Reasonings (MoR)

MoR published in July 2025⁵ proposes a mechanism where AI automatically selects different reasoning styles depending on the task.

• Embeds diverse reasoning patterns (deductive, inductive, analogical, etc.) into the model
• Automatically selects optimal reasoning method for the task without manual prompt engineering
• Experiments achieved 2.2% accuracy improvement compared to CoT prompts

Critical Representation Fine-Tuning (CRFT)

CRFT presented at ACL 2025⁶ is a method that precisely adjusts only critical parts of the model’s internal representations.

• Identifies “critical representations” through information flow analysis
• Achieves significant improvements with just 0.016% parameter adjustment
• 18.2 point accuracy improvement on GSM8K (math benchmark)
• 16.4% improvement even with one-shot (learning from just 1 example)

These studies suggest future AI models could perform more efficient and reliable reasoning.

Summary

Coding assistants like Claude Code are powerful tools, but we need to recognize that current AI reasoning has fundamental limitations.

Main problems:

1. Overthinking: Excessively reasoning even on simple problems, wasting resources or heading in wrong directions
2. Comprehension-competence gap: Can correctly explain rules but fails to apply them in actual tasks

Practical countermeasures:

1. Set clear task boundaries
2. Split complex tasks into small units
3. Always verify output especially for numerical processing and edge cases
4. Explicitly state project-specific rules in Custom Instructions
5. Interrupt reasoning and redirect as needed

AI appears good at “thinking,” but metacognitive ability to judge when and how much to think is still developing. As developers, it’s important to critically evaluate AI output and use it appropriately.

References

Reference materials corresponding to in-text citation numbers, listed in order.

Additional References (Not Numbered in Text)

• Top AI Research Papers of 2025: From Chain-of-Thought Flaws to Fine-Tuned AI Agents - AryaXAI (2025). [Reliability: Medium]
• Towards Reasoning Era: A Survey of Long Chain-of-Thought - Research Project Site (2025). [Reliability: Medium-High]

1. Chain of Thought Prompting in AI: A Comprehensive Guide - orq.ai (2025). [Reliability: Medium-High] ↩︎

2. The overthinking problem in AI - Amazon Science (2025). [Reliability: High] ↩︎ ↩︎²

3. AI Developers Look Beyond Chain-of-Thought Prompting - IEEE Spectrum (2025). [Reliability: High] ↩︎ ↩︎²

4. Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning - arXiv (2025). [Reliability: Medium-High (Preprint)] ↩︎ ↩︎²

5. Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies - arXiv (2025). [Reliability: Medium-High (Preprint)] ↩︎

6. Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning - ACL 2025. [Reliability: High (Peer-reviewed)] ↩︎