Preventing Claude 4 Sonnet Hallucination in Cursor

How many tokens should I include in Cursor to prevent Claude Sonnet hallucinations?¶

To prevent Claude 4 Sonnet from hallucinating when using Cursor, the amount of context provided (within its 200,000-token input limit and your specified 60-token output) depends on the task’s complexity and the model’s need for relevant information. Hallucination occurs when Claude lacks sufficient or precise context, causing it to generate incorrect or fabricated code or responses. Below, I’ll outline how much context is needed to minimize hallucination for various coding tasks, focusing purely on token counts and context relevance, without cost considerations.

Recommended Context for Minimal Hallucination¶

The goal is to provide enough relevant context to anchor Claude’s reasoning without overwhelming it. Here’s a breakdown by task type, with token estimates ~5 tokens per line of code

Small Tasks (e.g., Fixing a Function, Writing a Short Script):

• Context Needed: 1,000–5,000 tokens (~200–1,000 lines of code).
• Why: Small tasks require only the specific function, class, or file being modified, plus minimal dependencies. Too little context (e.g., <500 tokens) may omit critical variables or imports, leading to hallucinated code.
• Example: To fix a bug in a 200-line Python function, include the function (~1,000 tokens) and its immediate dependencies (e.g., imports, related config, ~500–1,000 tokens).
• Prompt Tip: Use precise prompts (e.g., “Fix the calculate_total function in @utils.py”) and reference specific files with Cursor’s @symbol to limit scope.
• Hallucination Risk: Low if the function and its dependencies are fully provided; high if context omits key variables or logic.

Medium Tasks (e.g., Refactoring a Module, Analyzing 2–3 Files):

• Context Needed: 10,000–50,000 tokens (~2,000–10,000 lines of code).
• Why: Refactoring or multi-file tasks require the target module(s), related files (e.g., utilities, configs), and sometimes a project overview. Too little context (e.g., only one file) may cause Claude to invent dependencies or misinterpret interactions.
• Example: To refactor a 2,000-line module, include the module (~10,000 tokens), its dependencies (e.g., 1–2 related files, ~5,000–10,000 tokens), and a brief project description (~500 tokens).
• Prompt Tip: Specify the scope (e.g., “Refactor @auth.js and its dependencies in @utils.js”) and avoid including unrelated files.
• Hallucination Risk: Moderate if dependencies are missing; low with complete, relevant context.

Large Tasks (e.g., Full Codebase Analysis, App Development):

• Context Needed: 100,000–200,000 tokens (~20,000–40,000 lines of code).
• Why: Complex tasks like architectural refactoring or cross-file dependency mapping require the entire codebase or large portions of it. However, including the full 200,000 tokens is often unnecessary and may dilute focus, causing Claude to misprioritize or invent connections.
• Example: For a 20,000-line app, include key modules (~50,000 tokens), critical dependencies (~30,000–50,000 tokens), and a project overview (~1,000 tokens). Exclude irrelevant files (e.g., tests, docs) unless directly needed.
• Prompt Tip: Use Max mode for large tasks, and filter context with Cursor’s file selection or explicit instructions (e.g., “Analyze @src folder, exclude @tests”).
• Hallucination Risk: High if critical files are omitted; moderate if too much irrelevant context is included.

General Guidelines to Minimize Hallucination¶

• Start with Minimal Context: Begin with 5,000 tokens (e.g., 1,000 lines, covering the target file/function and immediate dependencies). This is often sufficient for small to medium tasks and keeps Claude focused.
• Scale Incrementally: If Claude’s 60-token output is incorrect or hallucinated, add more context (e.g., 10,000–20,000 tokens) to include related files or configurations. For example, if a function fix fails, include the calling code or parent module.
• Use Precise Prompts: Specify the task and scope clearly (e.g., “Generate a 60-token fix for the login function in @auth.js using @config.js”). Vague prompts (e.g., “Fix my code”) force Claude to guess, increasing hallucination.
• Leverage Cursor’s Features: Use @file references to include only relevant files. Avoid letting Cursor’s Auto mode pull in excessive context (e.g., entire codebase for a single bug fix).
• Include Dependencies: Always provide imports, configurations, or related functions/classes that the task depends on. For example, a 60-token output fixing a function needs the function’s context (~1,000 tokens) and its dependencies (~500–2,000 tokens).
• Project Overview for Large Tasks: For tasks needing >50,000 tokens, include a 500–1,000-token summary of the project’s structure or purpose to guide Claude’s understanding without overloading it.

Suggestion¶

Before writing any code, adopt a Project Manager (PM) mindset and prepare accordingly. Proper planning helps reduce hallucinations and improves output accuracy. Here’s what you should do:

• Create a Product Requirements Document (PRD), Generate task, and Process Task:
  Keep each .mdc file under 900 tokens, with a total project-wide limit of 3,000 tokens. This ensures Claude has just enough context without being overloaded.
• Use Glob Patterns Efficiently:
  Leverage Cursor's support for globs to include or exclude specific file types with precision. For example:
  • *.spec.* → Focus on test files.
  • *.tsx → For Next.js or React component files.
  • *.config.js → For configuration files.
• Define Clear File Rules:
  Add filtering rules that target only relevant files needed for the task. This prevents Claude from being distracted by unrelated files or folders and keeps context tight.

Baseline Context Recommendation¶

• Default Context: 5,000–20,000 tokens (~1,000–4,000 lines) for most tasks. This covers 1–4 files, including the target code and its dependencies, and is sufficient for short outputs (60 tokens) like code snippets or confirmations.
• When to Use More:
  • 10,000–50,000 tokens: For multi-file tasks (e.g., refactoring a module with dependencies).
  • 100,000–200,000 tokens: Only for complex tasks requiring full codebase analysis (e.g., architectural changes). Filter out irrelevant files to stay under 150,000 tokens if possible.
• When to Use Less: If the task is highly focused (e.g., editing a single 100-line function), 1,000–2,000 tokens may suffice, provided dependencies are included.

Example Scenarios¶

Bug Fix in a Function:

• Context: 1,000–2,000 tokens (function: ~500 tokens, imports/config: ~500–1,000 tokens).
• Why: Claude needs the function and its dependencies to avoid hallucinating variable names or logic.
• Prompt: “Fix the calculate_total function in @utils.py using @config.py.”

Refactor a Module:

• Context: 10,000–20,000 tokens (module: ~10,000 tokens, 1–2 related files: ~5,000–10,000 tokens).
• Why: Claude needs the module and its dependencies to understand interactions and avoid inventing code.
• Prompt: “Refactor @auth.js using dependencies in @utils.js.”

Analyze a Codebase:

• Context: 100,000 tokens (key modules: ~50,000 tokens, dependencies: ~50,000 tokens).
• Why: Claude needs broad context but not irrelevant files to avoid misinterpreting relationships.
• Prompt: “Analyze @src folder for dependency issues, exclude @tests.”