Course: Elite AI Assisted Coding
Course Overview
Duration: 2 weeks, 6 intensive sessions
Format: Cohort-based course
Goal: Transform generic AI assistants into personalized coding partners that understand your codebase, patterns, and needs
Core Value Proposition
Stop wasting time with generic AI suggestions. Learn to create a personalized AI coding partner that actually understands YOUR specific context and requirements, regardless of which AI tool you use (Cursor, Copilot, Amp, Claude Code, Windsurf, etc.).
Instructors
Eleanor Berger: Engineering and AI leader with experience in DevOps/SRE/Cloud, Applied AI, and Engineering Leadership.
Isaac Flath: Dev efficiency expert with experience at big tech, open source, and advising companies.
Companies We've Helped
SpecStory
Travel and Leisure
Cable & Wireless Communications
GitHub
Microsoft
Google
Canonical
Answer AI
and many more …
Companies Our Students Are From
Amazon (AWS)
Microsoft
X
DocuSign
Monster
TrustLayer
and many more …
Curriculum
Mise en Place: Preparing tools, environments, and context for working with AI
Rules and config setup
Documentation strategies
Tools & environments configuration
LLM as assistant paradigm
Good Vibrations: Interactive AI coding
AI coding IDEs and tools comparison
Interactive agents and workflows
Real-time collaboration patterns
While You Were Gone: Delegating work to background agents and workflows
Background agents configuration
CI/CD workflows with AI
Autonomous task delegation
Weekly Session Breakdown
Week 1: Foundation & Personalization
The Context Progression Journey
Move from Generic → Curated → Personalized context
Real before/after examples showing 10x effectiveness improvements
Mise en Place principles for AI setup
AI Assisted Development Toolkit
Comprehensive tool comparison and selection guide
Hands-on evaluation of latest tools worth trying
Interactive AI coding patterns
Personalizing Your Static Context
Practical examples and transformations
Context curation best practices
Efficient management techniques
Rules and configuration management
Week 2: Automation & Advanced Techniques
Automating Updates from Chat Interactions
Tools for automatic context evolution
Mining conversation history for improvements
Pattern analysis to fix recurring AI failures
Model Context Protocol (MCP) and Agent-Based Context
Building practical MCP servers
Real-world examples used in daily development
Automated repetitive information gathering
Background agent architectures
Dynamic Context and Tools
Tool calling and dynamic information access
Building useful integrations for daily workflow
Production-ready tools useful for immediate projects
CI/CD integration strategies
Key Learning Outcomes
Technical Skills
Universal AI Setup System: Build context systems that work across ALL major AI tools (no vendor lock-in)
Automated Context Evolution: Automate context updates based on actual coding patterns
Pattern Mining & Analysis: Turn every AI mistake into a learning opportunity
MCP Server Development: Create practical automation tools for daily use
Practical Applications
Create and maintain context-independent rules across all major AI coding tools
Efficiently manage and sync context for different formats (Amp, Cursor, Copilot, Windsurf, Claude Code)
Analyze conversation history to identify and fix AI pattern failures
Build real integrations that drastically enhance workflow
Workflows
Plan and task-based agentic processes
Targeted human augmentation approaches
Matching tasks to optimal AI assistance methods
Enterprise deployment strategies and processes
Enterprise Integration
Implementing AI coding tools in enterprise settings
Security and compliance considerations
Team adoption strategies
Scaling personalized context across organizations
Integration with existing development workflows
Target Audience
Developers who:
Use AI coding assistants but feel limited by generic suggestions
Want to maximize productivity with personalized AI tools
Need a vendor-agnostic approach to AI coding assistance
Seek practical, production-ready solutions over theoretical concepts
Know AI should be better, but doesn't see how to get there
Course Philosophy
Focus on real-world applications
Vendor-agnostic approach ensures long-term value
Continuous improvement through automated pattern analysis
Practical tools you'll use daily in production environments