explaingit

deepmodel-ai/ai-sdlc

★ 17Audience · developerComplexity · 1/5Setup · easy

TLDR

A documented framework for software teams that structures AI-assisted development around explicit human control points, keeping developers in charge of every decision while the AI handles implementation work between checkpoints.

Mindmap

mindmap
  root((ai-sdlc))
    What it does
      Structure AI workflow
      Define control points
      Keep humans in charge
    Core documents
      Manifesto
      Principles
      In Practice guide
    Key concepts
      Explicit checkpoints
      Developer ownership
      Recorded decisions
    Target users
      Developers
      Engineering managers
      AI-assisted teams
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

Things people build with this

USE CASE 1

Adopt a structured process so your team understands every feature the AI builds before it ships.

USE CASE 2

Set up explicit review checkpoints in your AI-assisted workflow to prevent momentum without direction.

USE CASE 3

Share a documented engineering philosophy across your team for how to divide work between developers and AI tools.

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

The AI SDLC is a framework and manifesto for teams building software with AI assistance. Its starting premise is that AI tools have made it much easier to generate code quickly, but that speed creates its own problems: teams end up with more features than they fully understand, more code than they can reason about, and momentum without a clear direction. The framework is a response to that situation. The core idea is to structure development around explicit control points. At each control point, the developer pauses, reviews what the AI produced, verifies they understand it, and decides whether to move forward. AI handles the implementation work between those checkpoints. The developer retains ownership of every decision, and those decisions are recorded in the repository as shared context the team can refer back to. The README is intentionally brief and points to separate documents for the full detail: a Manifesto describing the reasoning behind the approach, a Principles file laying out the key rules, and an In Practice file covering the specific phases, verification steps, and how responsibility is divided between the developer and the AI. This is not a code library or a tool you install. It is a set of documented practices and a philosophy for engineering teams who want to use AI without losing understanding of what they are shipping. The language of the repo suggests it is aimed at developers and engineering managers who feel that AI-assisted workflows are moving faster than their teams can track. The repository itself is sparse on technical detail in the README, and most of the substance lives in the linked markdown files rather than in deployable software.

Copy-paste prompts

Prompt 1
I use Cursor and Copilot and my team ships features we don't fully understand. Walk me through applying the AI SDLC control-point pattern on our next sprint.
Prompt 2
Based on the AI SDLC Principles, review how my current AI-assisted workflow divides responsibility between the developer and the AI at each phase.
Prompt 3
I want to introduce the AI SDLC framework to my engineering team. Draft a short summary of the Manifesto I can share in our next team meeting.
Prompt 4
Walk me through the AI SDLC verification step from the In Practice file and show me how to apply it to a feature I just built with AI assistance.
Open on GitHub → Explain another repo

← deepmodel-ai on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.