Mishkin Method: AI-First SDLC

A smarter, faster approach to building real products with real users

Mishkin Method - AI First SDLC/PDLC
Mishkin Method – AI First SDLC/PDLC

 

Jump to:
Discover
Prototype
Validate
Deliver
Evolve
Guiding Principles
Glossary
Roles Matrix

Discover

Goal: Understand the problem, align on outcomes, and generate ideas.

Product Responsibilities

  • Use AI to generate personas, workflows, pain points, and goals.
  • Draft vision decks, problem statements, and feature descriptions.
  • Collect competitive intelligence and benchmarks using AI.

Tech Responsibilities

  • Expose system documentation, historical data, and API catalogs in formats AI can ingest.
  • Identify data sources that AI can summarize.

AI Tools

  • GPT/Claude for user story creation
  • Hero.ai for flow mockups
  • Semantic search (LangChain, Retrieval plugin)
  • Web scraping tools
  • Vector DBs for historical context

Prototype

Goal: Rapidly create an MVP version of the feature or flow for early feedback.

Product Responsibilities

  • Use Hero.ai to mock flows and interface ideas.
  • Use Replit to prototype logic and hook into shared libraries.

Tech Responsibilities

  • Provide shared libraries in Replit for auth, data access, API clients, etc.
  • Create sample data and scaffolds for fast prototyping.
  • Package usage documentation consumable by AI.

AI Tools

  • Replit for interactive prototypes
  • GPT/Claude for code generation and architectural scaffolding

Validate

Goal: Get feedback early and refine direction. This phase focuses on validating the MVP with real users to ensure the product is ready for production.

Product Responsibilities

  • Use AI to simulate user flows or generate demos.
  • Conduct real user testing in a live Replit environment to validate experience and assumptions.
  • Incorporate qualitative and behavioral feedback into prioritization.

Tech Responsibilities

  • Set up sandbox or Replit environments for demos and user testing.
  • Support instrumentation for capturing usability data.

Deliver

Goal: Productionalize the validated MVP. This phase marks the handoff from prototype to a hardened, secure, monitored product.

Tech Responsibilities

  • Cut over to real data sources or staging environments.
  • Harden code for security, quality, and performance.
  • Set up CI/CD pipelines using Replit as the local development environment.
  • Install observability and business tools (e.g., logging, monitoring, analytics, support hooks).
  • Ensure infrastructure, secrets, and environment configs are production-ready.
  • Coordinate QA agents to test the production version in staging.

Product Responsibilities

  • Support test coverage by coordinating acceptance criteria.
  • Confirm tooling for customer support, analytics, and communications are in place.

Evolve (Post-Delivery Phase: Fix, Enhance, and Expand)

Goal: After the product is delivered, Evolve becomes the ongoing phase that includes bug fixing, performance monitoring, feature enhancements, and the implementation of new feature sets. It uses the same tools (like Replit and AI copilots) to continually support, adapt, and extend the product.

  • Enhance: Improve and extend existing functionality.
  • Fix: Identify and resolve bugs or performance issues.
  • Expand: Introduce new feature sets — when substantial, these should trigger a full SDLC cycle.

Guiding Principles

  • AI is a teammate, not a tool.
  • Product leads with clarity, not specs.
  • Tech builds for reuse.
  • Real users, real feedback.
  • Evolve never ends.

Glossary

  • MVP: Minimum Viable Product – a stripped-down version focused on validating the core idea.
  • Replit: A live dev environment for collaborative prototyping, debugging, and development.
  • Hero.ai: A tool for mocking and exploring UI/UX flows visually with AI assistance.
  • AI Copilot: GPT/Claude acting as assistants for code, test, writing, or strategy generation.
  • Productionalize: Transforming a validated MVP into a secure, monitored, production-ready system.

Roles Matrix

  • Product: Discover (Lead), Prototype (Lead), Validate (Lead), Deliver (Support), Evolve (Lead)
  • Tech: Discover (Support), Prototype (Build), Validate (Support), Deliver (Lead), Evolve (Lead)
  • AI: Discover (Assist), Prototype (Co-Build), Validate (Analyze), Deliver (Generate), Evolve (Co-Build)
  • Users: Discover (Inform), Prototype (Review), Validate (Test), Deliver (Observe), Evolve (Use + Feedback)