Project Preparation with AI: From Idea to Documentation

The most important stage: Preparation of the project

** Target of the phase** Ensure the exact match of the generated code to the original design, minimize the number of iterations and prevent uncontrolled proliferation of functionality.

Problem in the absence of training

• Build an online store leads to a typical solution based on the most frequent patterns in AI training data.
• AI applies “standard” stack and architecture, ignoring the limitations of the project.
• Result: 5-15 iterations of refinements, loss of time, deviation from MVP, decreased motivation.

Solution: Automated training through AI In 2026, the entire stage of analysis and documentation can be delegated to AI offline. One request provides:

• market and niche analysis
• comparison
• target audience definition
• mVP specification
• ready-made Markdown documents

Recommended start request (template)

Task: fully autonomous preparation of the project "[short name]".

Follow the following steps:

1. Niche analysis:
- market volume (global / RF / CIS, forecast for 2026-2030)
Key trends and growth drivers
Seasonality, risks, barriers to entry

2. Competitive analysis:
5-8 major players (local + global)
strengths, weaknesses, unique features
UX/UI features that can be taken/improved

3. Target audience:
Demography, psychography, pain, solvency

4. MVP specification:
- mandatory pages/screens
- must-have functions
Explicit exclusions (what not to do in v1)

5. Technical limitations and preferences:
Priority of the mobile version
- allowable stack (or wishes)
- budget/time frame

6. Generate files in Markdown format:
- PROJECT.md (the structure according to the template below)
- Competitors. md
FEATURES.md (templates for 3-5 key features)
AGENTS.md (AI Rules: Code Style, Prohibitions, Limits)

PROJECT.md format:
## [Name of the project]

## Description
...

## Target audience
...

MVP functionality
...

## Exceptions v1
...

## Technical requirements
...

Structure of basic documents (recommended set)

• docs/PROJECT.md – the main document of the project
• docs/COMPETITORS.md – Market and Competitor Analysis
• docs/FEATURES.md - Feature specifications (one per section)
• docs/AGENTS.md - Code generation rules (limitations, style, prohibitions)
• docs/ARCHITECTURE.md - directory and stack structure (generated later)
• README.md - Start and Environment Instructions

The process after receiving documents from AI

1. Read and make clarifications (for example: “remove registration”, “add support for the Russian language in prints”).
2. Ask the AI to update the files for clarifications.
3. Save to the repository in the docs/ folder.
   code copy

Context: PROJECT.md and AGENTS.md attached. [Inserting content or linking files] Task:.

**Results of the automated approach**

- Reducing iterations from 8-15 to 1-3 per feature
- Fixing MVP borders to prevent scope creep
- Full Context for AI → Generating Code Corresponding to Design
- The ability to return to the project in months without losing knowledge

**Withdrawal**
Project preparation (analysis + documentation) is a stage that can be almost fully automated in 2026.

This gives the maximum ROI from using AI: less time for refinements, more time for implementation and iteration in essence.