Model Context Length - flight505/ContextCraft GitHub Wiki

Model Context Length

Understanding model context length is crucial for effective use of ContextCraft. This page explains what context length is, how it varies across AI models, and how to optimize your interactions within these constraints.

What is Context Length?

Context length (or context window) refers to the maximum number of tokens an AI model can process in a single interaction. This includes:

  1. The prompt or instructions you provide
  2. Any code or text you include as context
  3. The AI's generated response

The model can only "see" and reason about information within this context window. Anything beyond it is inaccessible to the model.

Context Windows for Common AI Models

ContextCraft supports various AI models, each with different context length capabilities:

Model Approximate Context Length Notes
GPT-3.5 16,000 tokens Good balance of performance and context
GPT-4 8,000 tokens Higher quality reasoning with smaller window
GPT-4-32k 32,000 tokens Extended context for complex codebases
GPT-4o 32,000 tokens Latest model with optimal context handling
Claude 2 100,000 tokens Very large context window
Anthropic Claude 3 200,000 tokens Extremely large context window
Code Llama Varies by version Open source alternative

Note: Context lengths may change as models are updated. ContextCraft stays current with the latest limits.

How Context Length Affects Code Analysis

Context length directly impacts what you can accomplish with AI code assistance:

Limited Context (8K-16K tokens)

With smaller context windows, you'll need to:

  • Focus on specific files or functions
  • Use aggressive code compression
  • Prioritize the most relevant code
  • Break larger questions into smaller, focused queries

Medium Context (32K-64K tokens)

With medium context windows, you can:

  • Include multiple files from a project
  • Provide more implementation details
  • Ask more comprehensive questions
  • Maintain better context across related questions

Large Context (100K+ tokens)

With large context windows, you can:

  • Include substantial portions of a codebase
  • Provide rich documentation alongside code
  • Analyze complex interactions between components
  • Maintain conversational history while examining code

Context Management in ContextCraft

ContextCraft offers several features to help manage context effectively:

Context Visualization

  • Real-time token counting shows current usage
  • Progress bars indicate how close you are to limits
  • Visual warnings appear when approaching context limits
  • Model-specific indicators adjust based on selected model

Context Optimization

  • Code compression reduces token usage by abstracting implementation
  • Comment removal eliminates non-essential text
  • Smart file selection helps prioritize relevant code
  • Token budgeting assists in planning context allocation

Model Selection

  • ContextCraft allows switching between supported AI models
  • Displays the available context for each model
  • Suggests appropriate models based on your selection size
  • Optimizes prompts for specific model capabilities

Context Window Best Practices

Strategic File Selection

  1. Prioritize Key Files:

    • Select files directly related to your question
    • Include interface definitions and type declarations
    • Add files with core functionality first

  2. Create Context Layers:

    • Layer 1: Files essential to understand the problem
    • Layer 2: Supporting files that provide context
    • Layer 3: Optional files if space permits

Effective Prompting

  1. Be Specific:

    • Ask focused questions about the included code
    • Reference specific files or functions in your question
    • Break complex questions into smaller parts

  2. Provide Clear Instructions:

    • Tell the AI what files are included and why
    • Explain any context optimization you've applied
    • Set clear expectations for the response format

Context Conservation

  1. Minimize Boilerplate:

    • Exclude generated code when possible
    • Skip test files unless directly relevant
    • Omit third-party dependencies unless modified

  2. Use Progressive Loading:

    • Start with a minimal set of files
    • Add more context only when needed
    • Retain important information across interactions

Handling Large Codebases

When working with codebases that exceed context limits:

Segmentation Strategies

  1. Functional Segmentation:

    • Divide the codebase by feature or module
    • Focus on one functional area at a time
    • Connect insights across multiple interactions

  2. Layered Analysis:

    • First pass: High-level architecture (with compression)
    • Second pass: Specific implementation areas (without compression)
    • Final pass: Detailed analysis of key components

Multi-Session Approaches

  1. Continuous Sessions:

    • Maintain conversation history across multiple queries
    • Reference previous insights in new questions
    • Build a comprehensive understanding incrementally

  2. Summary and Drill-Down:

    • Start with a compressed overview of the entire codebase
    • Identify areas for deeper investigation
    • Explore specific sections with more detail in follow-up sessions

Technical Implementation Details

Token Counting Accuracy

ContextCraft's token counter:

  • Uses tokenization algorithms similar to those in AI models
  • Provides language-specific tokenization rules
  • Offers accurate estimates even for mixed content
  • Accounts for special tokens used by different models

Context Packaging

When sending content to AI models, ContextCraft:

  • Optimizes prompt structure for efficient token usage
  • Applies appropriate model-specific formatting
  • Includes metadata about applied optimizations
  • Preserves critical structural information

Advanced Context Techniques

Custom Context Templates

Power users can create custom context templates:

  • Define fixed context elements
  • Set up template variables
  • Create reusable context structures
  • Optimize for specific types of questions

Context Memory

Some versions of ContextCraft include context memory features:

  • Save important context for reuse
  • Define persistent context elements
  • Track context usage across sessions
  • Optimize context based on past interactions

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