Everything about AI SDLC in one place
Simple explanations of the AI SDLC platform, model-driven development with LLMs, artifact chains, traceability, reverse engineering and AI-assisted delivery.
Introduction
What is AI SDLC
AI SDLC is a practical framework for using LLMs across the software development lifecycle: requirements, use cases, architecture, development, testing and documentation.
ReadHow AI SDLC works
AI SDLC uses controlled transformations: every artifact has an input, a template, rules, context and validation criteria.
ReadFull AI-assisted development cycle
The full cycle covers idea, requirements, use cases, architecture, code-generation briefs, testing, documentation and reverse engineering.
ReadTransition to AI SDLC
Teams can adopt AI SDLC on a new product, migrate an existing system or use a hybrid approach with new features first.
ReadResults of adopting AI SDLC
The main effects are faster routine work, fewer defects, current documentation, easier onboarding and better traceability.
ReadOperating model
SDLC operating model
The operating model defines stages, roles, artifacts, validation points and approval rules for AI-assisted delivery.
ReadDescriptive models and artifacts
Descriptive models are structured artifacts that represent what the product must do, how it is built and how it is checked.
ReadSDLC stages and practices
AI SDLC splits work into practices: concept, stakeholder requirements, specification, use cases, architecture, testing and documentation.
ReadConfiguration and control objects
Configuration objects define templates, rules, context and validation criteria that control AI generation.
ReadRoles and responsibilities
AI SDLC gives every role an AI assistant for its own artifacts, while humans stay responsible for decisions and approvals.
ReadCompatibility with management models
AI SDLC can work with Scrum, Kanban, Waterfall, hybrid delivery and startup MVP workflows.
ReadModules
Analytics module
The Analytics module turns an idea into requirements, user stories, use cases, domain terms and traceability.
ReadArchitecture module
The Architecture module turns requirements into components, interfaces, API contracts, diagrams and technical constraints.
ReadDocumentation module
The Documentation module creates and updates user documentation, technical documentation, API docs and release notes from current artifacts.
ReadTesting module
The Testing module generates test cases and coverage from requirements, architecture and code, with traceability to source artifacts.
ReadAnalytics practices
Product or feature concept
A concept describes the business problem, target users, value proposition, success metrics and first scope.
ReadStakeholder requirements
Stakeholder requirements capture what different users and business stakeholders need before the system specification is written.
ReadRequirements specification
A requirements specification turns stakeholder needs into clear functional and non-functional requirements.
ReadUse case identification
Use case identification finds actors, goals and system boundaries before detailed use case specifications are written.
ReadUse case specification
A use case specification describes the main flow, alternative flows, errors, preconditions, postconditions and acceptance criteria.
ReadDomain model development
A domain model defines business entities, attributes, relationships and terms that the whole team should use consistently.
ReadRequirements traceability matrix
A traceability matrix connects requirements, use cases, architecture elements, tests and documentation.
ReadTemplates and prompts
Use cases
AI SDLC for managers
Managers use AI SDLC to reduce delivery risk, improve visibility, shorten onboarding and keep project knowledge explicit.
ReadWhere AI SDLC can be used
AI SDLC is useful for MVPs, internal tools, enterprise systems, legacy documentation, product discovery and AI-assisted coding workflows.
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