GenAI Development Techniques — Comparison
A comprehensive, evidence-based comparison of techniques, methodologies, and frameworks for structured AI-assisted software development. The focus is on how humans organize and direct AI coding agents — not on the AI models or tools themselves.
Start Here
Choosing Your Approach — Which technique fits your situation? Decision guide by team size, project type, industry, and development activity.
Overview & Comparison Matrix — Executive summary, full comparison table, and category analysis.
Deep-Dive Documents
Decision Guide
| Document |
Description |
| Choosing Your Approach |
Which technique for which situation — by team size, project type, industry, methodology, and task type |
Spec-Driven Development
| Technique |
Description |
Stars |
| GSD (Get Shit Done) |
Meta-prompting, context engineering, and spec-driven dev system for reliable AI development |
~64K |
| Spec Kit |
GitHub's official toolkit for spec-driven development — specs → plans → tasks |
~114K |
| OpenSpec |
Change-centric SDD with delta specs — brownfield-first, 27+ tool support, YC W26 |
~56K |
Multi-Agent Orchestration
| Technique |
Description |
Stars |
| Squad |
Coordinator-based multi-agent orchestration with persistent memory, casting, and ceremonies |
~2.8K |
| BMAD |
AI-driven agile framework with 12+ specialized agent personas and 34+ workflows |
~49K |
Skill-Based Development
| Technique |
Description |
Stars |
| Superpowers |
Composable skills framework — TDD, subagent-driven development, self-improving agent workflows |
~234K |
Autonomous Iteration
| Technique |
Description |
Stars |
| Ralph |
Autonomous bash-loop methodology — tests as backpressure, git as memory, tool-agnostic |
Community |
Enterprise AI-Native SDLC
| Technique |
Description |
Stars |
| HVE |
Microsoft ISE's RPI workflow with 49 agents, constraint-based governance, and validated artifacts |
~1.2K |
Cross-Cutting
| Technique |
Description |
| Context Engineering |
The practice of structuring project context via rules files across an 8-layer model |
Audience: Developers, tech leads, and engineering managers evaluating structured approaches to AI-assisted development.
What this is not: A ranking. Each technique serves different needs. The Decision Guide in the overview helps match techniques to situations.