ContextMemory

How to Give Full Code Context to AI Models

A technical guide to increasing AI context window for better code generation

Code Context Guide

Understanding Code Context

ContextMemory is a platform that enhances AI coding assistants by providing them with comprehensive, real-time context about your codebase. This allows the AI to generate code that perfectly matches your existing patterns, style, and architecture.

Unlike basic prompt techniques that only capture a small portion of your code, ContextMemory builds a comprehensive understanding of your entire repository, including structure, documentation, naming conventions, and coding patterns.

Key Difference: Most tools just provide the AI with the current file or a few lines of context. ContextMemory builds a semantic understanding of your entire codebase and intelligently selects the most relevant context for each prompt to increase the AI context window effectively.

Codebase Scanning

When you first connect ContextMemory to your repository, it performs an initial scan to build a knowledge model of your codebase:

  1. Structure Analysis: Maps the organization of files, directories, and modules
  2. Pattern Recognition: Identifies recurring code patterns, naming conventions, and architectural choices
  3. Documentation Indexing: Catalogs comments, docstrings, README files, and other documentation
  4. Dependency Mapping: Understands relationships between components and external libraries

This scanning process happens locally on your machine or within your secure infrastructure. No code is transmitted to our servers during this process.

// Example of pattern recognition
// ContextMemory identifies that your project consistently uses:

// 1. Error handling with specific patterns
try {
  // operation
} catch (error) {
  logger.error('Context:', error);
  throw new AppError(error.message);
}

// 2. Naming conventions
const fetchUserData = async (userId) => { ... }  // Not getUserInfo or retrieveUser

Memory Context Injection

When you interact with an AI coding assistant, ContextMemory intelligently selects and injects the most relevant context into the prompt:

What gets included:

  • Relevant file structures and imports
  • Similar code patterns from your codebase
  • Project-specific naming conventions
  • Error handling patterns
  • Documentation style guidelines
  • Related utility functions

Optimization techniques:

  • Token-aware summarization
  • Relevance ranking algorithms
  • Context window management
  • Semantic similarity matching
  • Dynamic prompt construction

This injection process is dynamic and adapts to each specific coding task. The AI doesn't just receive more context—it receives the right context, effectively increasing the AI model's context window.

// Simplified example of injected context
{
  "current_file": "src/services/user.js",
  "task": "Add a function to validate user credentials",
  "relevant_patterns": [
    {
      "file": "src/services/auth.js",
      "functions": ["validateToken", "hashPassword"]
    },
    {
      "file": "src/utils/validation.js",
      "functions": ["isValidEmail", "hasMinLength"]
    }
  ],
  "naming_conventions": {
    "functions": "camelCase, verb + noun",
    "constants": "UPPER_SNAKE_CASE"
  },
  "error_handling": "try/catch with logger.error and custom AppError"
}