AI-Assisted SDLC
How pair structures collaboration between developers and AI assistants across the full software development lifecycle.
When developers collaborate with AI coding assistants, a fundamental problem emerges: the AI lacks context. It does not know your architecture, your tech stack decisions, your team's standards, or where you are in the development process. Every session starts from zero.
The result is inconsistent code, hallucinated libraries, drifting decisions, and variable quality. The AI is powerful but directionless.
The pair Approach
pair solves this by providing structured, persistent context that AI assistants read at the start of every session. Instead of treating AI as a code autocomplete tool, pair treats it as a team member that needs onboarding.
This onboarding takes the form of a Knowledge Base — a set of documents installed in your project that cover:
- How to work — 11 process guides defining every step from product requirements to code review
- What standards to follow — guidelines for architecture, testing, code design, security
- What the team decided — adoption files recording the chosen tech stack, architecture, and way of working
The AI reads these documents and follows them consistently.
Structured, Not Ad Hoc
Traditional AI-assisted development is ad hoc: you prompt, the AI responds, and you hope the output aligns with your standards. pair replaces this with a structured lifecycle:
- Induction — one-time project setup: create a PRD, complete the bootstrap checklist, prioritize initiatives
- Strategic Planning — break initiatives into epics, define the architecture
- Iteration — break epics into user stories, refine acceptance criteria, create tasks
- Execution — implement tasks with TDD, review code against standards
Each level has specific process guides that the AI follows. The guides reference your adoption files, so the AI's output is always aligned with your decisions.
Why It Matters
| Problem | How pair solves it |
|---|---|
| Inconsistent code across sessions | The AI always reads the same adoption files |
| Hallucinated libraries and patterns | Tech stack adoption lists approved dependencies; review blocks deviations |
| Decisions not aligned with team | Every task references specific adoption documents |
| Unstructured process | 11 how-to guides define every step from idea to merge |
| Variable quality | Definition of Done checklist and automated quality gates |
| Slow onboarding | New developer (or AI) reads .pair/ and has full context |
How It Works in Practice
A developer starts a session and says: "I need to implement the user login feature."
The AI reads .pair/ and knows:
- The architecture is a monolith with REST API (from
architecture.md) - The tech stack uses TypeScript, React, and Vitest (from
tech-stack.md) - The team uses Kanban with squash merges (from
way-of-working.md) - The implementation guide requires TDD (from
how-to/10-how-to-implement-a-task.md)
It creates a feature branch, writes tests first, implements the minimum code to pass them, runs quality gates, and creates a PR following the team's template. Every step follows the documented process.
The next day, a different developer (or the same developer in a new session) picks up where things left off. The AI reads the same files and maintains the same standards.