From Prompts to Production: Live AI Coding Workshop

Workshop Formats

Start remote. Scale to on-site when your team needs hands-on work with your codebase.

Remote Half-Day Intensive

$5,000 (up to 10 engineers)

Four hours of hands-on training via Google Meet. Your team learns the core AI coding workflow patterns and applies them to your own codebase during the session.

• Context persistence: CLAUDE.md files, rules files, progressive disclosure
• Multi-agent orchestration: parallel agents, task decomposition, spec-driven development
• CI/CD integration: quality gates for AI-generated code, testing patterns, deployment guardrails

Outcome: Each engineer leaves with a working AI coding workflow they can use Monday morning.

Remote Full-Day Workshop

$10,000 (up to 15 engineers)

Seven hours covering the full lifecycle. Morning: structured labs on all three pillars. Afternoon: your team builds a working prototype using real business problems.

• Everything in the half-day intensive
• Hands-on prototype build using your team's actual codebase
• Team workflow documentation and standards template
• Recorded session for internal reference

Outcome: Your team has standardized AI coding workflows, a working prototype, and documentation they can share across the organization.

On-Site Team Immersion

Custom pricing based on team size and scope. Book a call to scope.

Two days on-site. Day one: structured labs on AI coding patterns. Day two: hackathon on your real use cases with hands-on pairing.

• Everything in the full-day workshop
• Deep integration with your existing CI/CD pipeline
• Pairing sessions on your production codebase
• Post-workshop readiness assessment and adoption roadmap

Outcome: Your team has battle-tested AI coding workflows integrated into your actual development environment.

What Your Team Learns

Three pillars that take AI coding from ad-hoc prompting to production-grade engineering.

1. Context Persistence (Pre-Generation)

AI tools produce better code when they understand your codebase, conventions, and constraints. Your team learns to build context that persists across sessions and contributors.

• CLAUDE.md and rules files that encode your team's standards
• Progressive disclosure for large codebases
• Repository organization that AI tools can navigate

2. Multi-Agent Orchestration (Generation)

Single-prompt coding hits a ceiling fast. Your team learns patterns for decomposing work across multiple AI agents working in parallel.

• Spec-driven development: write the spec, let agents implement
• Task decomposition and parallel agent coordination
• Multi-file generation workflows that stay consistent

3. CI/CD Integration (Post-Generation)

AI-generated code that passes a vibe check in a PR but fails in production is worse than no AI at all. Your team learns the quality gates that keep your pipeline trustworthy.

• Testing patterns for AI-generated code
• Security and dependency analysis guardrails
• Code review workflows for AI-assisted PRs
• Quality gates that catch what AI gets wrong

What Your Team Walks Away With

• A repeatable AI coding workflow tailored to your stack
• Context files (CLAUDE.md, rules) configured for your codebase
• A GitHub repository with code, docs, and tests from the session
• Recorded demos for internal review and onboarding
• A pattern library that reduces build time on future projects
• Post-workshop readiness assessment (full-day and on-site formats)

Who This Is For

• Tech leads and engineering managers adopting AI coding tools for their teams
• Teams using AI tools inconsistently and needing standardized workflows
• Engineering organizations that need CI/CD integration for AI-generated code
• Consulting and services firms where AI adoption drives delivery speed

Teams with existing CI/CD pipelines and code review practices see the fastest results.

Continued Learning

Keep building after the workshop with self-serve resources.

• Access to the workshop pattern library and all code samples
• Recorded session for team onboarding and reference
• Context files (CLAUDE.md, rules) configured during the workshop
• Updates when new AI coding patterns and tool integrations ship

Want structured, self-paced training? Join the course waitlist for the full From Prompts to Production curriculum.

What This Workshop Does Not Promise

• Production-ready software in a single session
• Replacement of your existing SDLC controls
• Deployment to regulated environments without further work

This workshop builds skills, patterns, and prototypes. Your team still follows your normal processes for security, data governance, and production deployment.

About the Instructor

Paul Duvall wrote the book on Continuous Integration. Literally. His Jolt Award-winning Continuous Integration: Improving Software Quality and Reducing Risk (Martin Fowler Signature Series) defined the discipline for a generation of engineers. Now he is building the playbook for CI/CD in the age of AI-generated code.

• CI/CD Pioneer: Authored the foundational book on Continuous Integration
• Company Builder: Co-founded Stelligent, scaling to nearly 100 enterprise customers, AWS Premier Partner status, and $10M+ annual revenue before a $25M exit
• AWS Engineering Leader: Led DevSecOps and Security Innovation teams at AWS (2021-2024)
• AWS Hero (2016-2021): Recognized for contributions to the cloud community
• AI Coding Practitioner: Three years of daily hands-on experience with AI coding workflows, building production patterns for context persistence, multi-agent orchestration, and CI/CD integration