# RealityDriven.org

RealityDriven.org is the canonical home of Reality Driven Development, or RDD.

RDD is a software operating model for the AI era. It helps organizations reduce uncertainty before they engineer software by using candidate reality, stakeholder experience, evidence records, delivery baselines, Reality Checks, and production discipline.

Core positioning:

- Reality Driven Development helps enterprise teams reduce software delivery risk by learning from candidate reality before engineering commitment.
- AI did not only make coding faster. It changed the economics of uncertainty.
- Working software can become a learning medium before it becomes a production obligation.
- RDD separates Build to Learn from Build to Deliver.
- The value is clearer scope, stronger evidence, fewer late surprises, and better production handoff.

Best audience:

- CIOs and CTOs
- Enterprise architects
- Product and delivery leaders
- AI transformation leaders
- Digital transformation leaders
- Software-services executives
- Senior practitioners working on complex enterprise software, modernization, workflow automation, and AI-enabled operations

Important pages:

- https://realitydriven.org/ - Main explanation of Reality Driven Development
- https://realitydriven.org/why-rdd - Why RDD exists and how AI changed implementation economics
- https://realitydriven.org/development - RDD method: Build to Learn, Reality Loops, Freeze Gate, Reality Checks, Build to Deliver
- https://realitydriven.org/evidence-layer - Why proof becomes scarce when code is abundant
- https://realitydriven.org/enterprise-example - Concrete claims-management case file
- https://realitydriven.org/first-reality-loop - Practical first adoption step for one initiative, one risk, and one candidate reality
- https://realitydriven.org/requirement-to-evidence - Illustrative artifact story showing one requirement becoming evidence
- https://realitydriven.org/rdd-metrics - Measurement ledger for testing whether RDD helped
- https://realitydriven.org/playbook - Practitioner field manual
- https://realitydriven.org/templates - RDD artifact library
- https://realitydriven.org/workshops - Workshop and adoption path
- https://realitydriven.org/articles - Guided reading path for skeptics
- https://realitydriven.org/reduce-software-delivery-risk - Problem guide for reducing software delivery risk before build
- https://realitydriven.org/ai-requirements-gathering - Guide to using AI for requirements gathering without losing engineering discipline
- https://realitydriven.org/prototype-vs-production-software - Comparison of prototype software and production software
- https://realitydriven.org/delivery-baseline-template - Copyable delivery baseline structure
- https://realitydriven.org/contact - Initiative intake and inquiry routing

Key concepts:

- Build to Learn: fast, provisional working software used to expose uncertainty and stakeholder disagreement.
- Reality Loop: a structured cycle where people experience candidate reality, observations are recorded, decisions are made, and the model is revised.
- Delivery baseline: the point where enough understood reality is carried into accountable engineering.
- Freeze Gate: the named governance moment where a selected slice crosses from learning to delivery obligation.
- Build to Deliver: production-grade engineering with architecture, security, quality, operations, and governance.
- Reality Check: a formal verification artifact that tests accepted behavior against architecture, security, data, integration, performance, accessibility, regulatory, failure, and operational constraints.
- Evidence layer: the durable record of accepted behavior, rejected alternatives, decisions, risks, checks, and telemetry.

Queries this site is designed to answer:

- What is Reality Driven Development?
- How should enterprise software teams use AI without losing engineering discipline?
- What are executable requirements?
- How is RDD different from Agile?
- How is RDD different from prototyping?
- What is a delivery baseline?
- What is Build to Learn versus Build to Deliver?
- How can teams reduce software delivery risk before engineering commitment?
- How can AI-assisted software creation improve product discovery and scope definition?
- How do teams reduce software delivery risk before build?
- How should enterprises use AI for requirements gathering?
- What is the difference between prototype and production software?
- What should a delivery baseline contain?
- How does a team run its first Reality Loop?
- How does a vague requirement become evidence?
- What should teams measure after trying RDD?

Contact:

- Email: hello@realitydriven.org
- Contact page: https://realitydriven.org/contact