Technical Documentation for AI: What Your Coding Tools Actually Need

You've been writing documentation wrong.

Not wrong for humans—your README might be great. Wrong for AI coding tools.

There's a reason why AI generates inconsistent code even when you "give it context." The context isn't formatted for how LLMs process information.

Let's fix that.

How AI Reads Documentation

Understanding this changes everything: AI coding tools don't read like humans.

Humans:

• Skim for relevant sections
• Infer from context and examples
• Fill in gaps with common sense
• Remember previous pages

AI:

• Processes linearly within context window
• Takes statements literally
• Needs explicit connections between concepts
• "Forgets" anything outside the current context

This means documentation that works for humans often fails for AI.

The Six Documents Every AI Project Needs

Through hundreds of LucidCode sessions, we've identified the six document types that make AI coding dramatically more effective:

1. Project Overview

Purpose: Ground the AI in what this project is and isn't.

Include:

• One-paragraph project description
• Core problem being solved
• Target users (specific, not generic)
• What's explicitly out of scope
• Key technology decisions

Example snippet:

Project: TaskFlow - a task management app for freelance designers

Core Problem: Freelancers lose track of deliverables across multiple
clients, leading to missed deadlines and lost income.

Target User: Solo freelance designers managing 3-8 active clients

Out of Scope (v1): Team features, invoicing, time tracking, mobile app

Tech Stack: React 19, TanStack Router, PostgreSQL, Drizzle ORM

2. User Personas

Purpose: Give AI a reference for who uses each feature.

Include:

• 2-3 distinct user types
• Their goals and frustrations
• How they'll use the system
• What they care about / don't care about

Why it matters: When AI knows "Sarah is a busy freelancer who checks tasks on her phone between meetings," it makes different design decisions than generic "the user."

3. App Flow Diagrams

Purpose: Map how users move through the system.

Include:

• Entry points (how do users arrive?)
• Critical paths (most common journeys)
• Decision points (where does behavior branch?)
• Exit points (when is the user "done"?)

Format tip: ASCII or text-based diagrams work better than image references (AI can't see images in most contexts).

4. Technical Architecture

Purpose: Define how components connect before code exists.

Include:

• Component hierarchy
• Data flow direction
• State management approach
• API contract outlines
• Database relationships

Example snippet:

Data Flow:
1. User action triggers React component event
2. Component calls TanStack Query mutation
3. Mutation hits API route in /api/
4. API route validates with Zod, writes to PostgreSQL via Drizzle
5. Response flows back, Query cache invalidates, UI updates

State: Server state via TanStack Query, minimal client state via React context

5. Database Schema

Purpose: Define data structures explicitly so AI generates consistent models.

Include:

• All tables/collections with fields
• Field types and constraints
• Relationships between entities
• Indexes and unique constraints
• Example data shapes

Why it matters: AI guessing your schema creates cascading inconsistencies. Every feature touches data—get this right first.

6. Development Guidelines

Purpose: Codify patterns so AI produces consistent code.

Include:

• File/folder naming conventions
• Component patterns (how to structure a new component)
• Error handling approach
• Naming conventions (variables, functions, etc.)
• What NOT to do (anti-patterns to avoid)

Example snippet:

Component Pattern:
- One component per file
- Export component as default
- Props interface named [ComponentName]Props
- Place hooks at top of component
- Extract logic over 10 lines to custom hook

Naming:
- Components: PascalCase
- Files: kebab-case
- Hooks: use[Name]
- API routes: [resource].route.ts

Formatting for AI Consumption

How you write matters as much as what you write.

Do:

Use explicit headers:

## Authentication Flow
When a user logs in, the following happens:
1. Form submits credentials to /api/auth/login
2. Server validates against database...

Be specific with references:

The UserCard component (src/components/user-card.tsx) displays...

State things directly:

All API responses use this shape:
{ success: boolean, data?: T, error?: string }

Don't:

Use vague references:

See the auth section for more details  // AI can't "see" other sections

Assume knowledge:

Handle errors the usual way  // What's "usual" to you isn't to AI

Use images for critical info:

See the diagram below  // AI can't process images in most contexts

The LucidCode Documentation System

This is exactly what LucidCode generates automatically.

After our AI co-founder conversation, you get all six document types—formatted specifically for AI coding tool consumption. No manual documentation. No guessing what to include.

The result: prompts that work the first time because AI has the context it actually needs.

Documentation Checklist

Before starting any AI coding session:

• [ ] Project overview defines scope and constraints
• [ ] User personas exist for each user type
• [ ] App flows are documented in text (not just images)
• [ ] Technical architecture specifies data flow
• [ ] Database schema is complete and explicit
• [ ] Development guidelines cover patterns and anti-patterns

Missing any of these? Your AI will guess. And AI guesses compound into architectural problems.