LLMS.txt Generator Guide: AI-Ready Project Documentation for Developers 2025

Core LLMS.txt Components:

1. Project Overview

• Purpose and goals: What the project does and why it exists
• Target audience: Who uses the software and how
• Key features: Main functionality and unique selling points
• Tech stack: Languages, frameworks, and tools used

2. Architecture Documentation

• System design: High-level architecture patterns
• Component relationships: How different parts interact
• Data flow: Information movement through the system
• External dependencies: Third-party services and APIs

3. Development Context

• Coding standards: Style guides and conventions
• Common patterns: Recurring code structures
• Business rules: Domain-specific logic and constraints
• Testing approaches: Testing strategies and frameworks

# LLMS.txt - Project Documentation

## Project Overview
**Name:** [Project Name]
**Purpose:** [Brief description of what the project does]
**Technology Stack:** [Primary languages and frameworks]
**Architecture:** [Microservices, Monolith, Serverless, etc.]
**Last Updated:** [Date]

## Quick Start
**Setup:** [Basic setup commands]
**Run:** [How to run the project locally]
**Test:** [How to run tests]

## Frontend Architecture
**Framework:** React 18 with TypeScript
**State Management:** Redux Toolkit + RTK Query
**Styling:** Tailwind CSS with custom components
**Build Tool:** Vite with hot module replacement

## Component Patterns
- Use functional components with hooks
- Custom hooks for business logic separation  
- Component composition over inheritance
- Consistent prop typing with TypeScript interfaces

## API Integration
**Base URL:** https://api.example.com/v1
**Authentication:** JWT tokens in Authorization header
**Error Handling:** Centralized error boundary + toast notifications
**Caching:** RTK Query for automatic cache management

## Service Architecture
**Framework:** Express.js with TypeScript
**Database:** PostgreSQL with Prisma ORM
**Authentication:** JWT with refresh token rotation
**Deployment:** Docker containers on AWS ECS

## Database Schema
**Users:** id, email, password_hash, role, created_at
**Products:** id, name, description, price, category_id
**Orders:** id, user_id, total, status, order_items[]

## Business Rules
- Users can only view their own orders
- Inventory must be checked before order creation
- Prices are stored in cents to avoid floating point issues
- Soft delete patterns used for audit trails

## Data Pipeline
**Input Sources:** CSV files, PostgreSQL, REST APIs
**Processing:** Pandas, NumPy, scikit-learn
**Models:** Random Forest, XGBoost, Neural Networks
**Output:** Predictions API, batch processing results

## Feature Engineering
- Categorical encoding using target encoding
- Numerical features scaled with StandardScaler
- Date features extracted: day_of_week, month, quarter
- Text features processed with TF-IDF vectorization

## Model Training
**Validation:** Time-series split for temporal data
**Metrics:** ROC-AUC for classification, RMSE for regression
**Hyperparameter tuning:** Optuna for optimization
**Model versioning:** MLflow for experiment tracking

• Consistent AI behavior: All team members get similar AI assistance quality
• Knowledge sharing: Implicit knowledge becomes explicit and accessible
• Onboarding acceleration: New developers understand project context faster
• Code review enhancement: AI can provide better context during reviews
• Documentation maintenance: Living documentation stays current with codebase