Learn how to turn standard AI assistants into true coding partners that understand your style, context, and project specifics. The course will teach you how to configure, adapt, and develop any AI tools (Cursor, Copilot, Amp, Claude Code, Windsurf, etc.) so that they work for you, not the other way around.
Elite AI Assisted Coding
19h 27m 48s
English
Paid
Course Value:
Stop wasting time on generic AI prompts. Learn how to create your own intelligent programming assistant that understands your code, architecture, and tasks—regardless of the platform you use.
What You Will Learn:
- Building a universal context system for all AI tools
- Automating context updates based on your real patterns
- Analyzing and improving interaction with AI to enhance accuracy
- Developing MCP servers and automation tools
- Integrating AI into corporate processes considering security and compliance
- Optimizing team collaboration and CI/CD with AI agents
Who This Course Is For:
Developers who:
- Already use AI for programming but want to take efficiency to the next level
- Aspire to personalized, productive AI tools
- Prefer practical skills and real results over theory
- Work in teams where integrating AI into corporate processes is important
Instructors:
- Eleanor Berger — an engineer and leader in the field of AI with experience at Microsoft and Google, expert in DevOps, Applied AI, and engineering management.
- Isaac Flatt — an expert in development efficiency, consultant to companies, and a researcher in AI.
Companies We Have Worked With:
Microsoft, Google, GitHub, Canonical, SpecStory, Travel & Leisure, Cable & Wireless Communications, Answer AI, and others.
Companies Our Students Come From:
Amazon (AWS), Microsoft, Google, X, Shopify, Cisco, LinkedIn, Red Hat, DocuSign, Qualcomm, Monster, Booz Allen Hamilton, TrustLayer, and more.
Bottom Line:
After the course, you will not only be able to use AI but build an effective development ecosystem around it—from individual productivity to team implementation in production.
About the Authors
Eleanor Berger
Eleanor Berger is an AI researcher and educator focused on the AI-coding workflow at the senior-engineer level — particularly the patterns for using AI-coding tools effectively in real engineering work rather than as side experiments.
Her CourseFlix listing carries Elite AI Assisted Coding — a structured treatment of using AI-coding tools (Claude Code, Cursor, Aider) at the level of someone who has integrated them deeply into their daily engineering practice, covering the prompt patterns, project-context strategies, and the workflow disciplines that separate effective AI-coding from spam.
Material is paid and aimed at experienced developers ready to make AI-coding tools a core part of their craft. For broader content, see CourseFlix's AI-Assisted Coding category page.
Isaac Flath
Isaac Flath is a US software engineer and AI educator (a long-running fast.ai community member) focused on the AI-assisted-coding workflow at the senior-engineer level.
His CourseFlix listing carries Elite AI Assisted Coding — a structured treatment of using AI-coding tools (Claude Code, Cursor, Aider) at the level of someone who has integrated them deeply into their daily engineering practice rather than treating them as side experiments.
Material is paid and aimed at experienced developers ready to make AI-coding tools a core part of their craft. For broader content, see CourseFlix's AI-Assisted Coding category page.
Watch Online 24 lessons
0:00
/ #1: 001 Introduction to AI-Assisted Software Development
All Course Lessons (24)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | 001 Introduction to AI-Assisted Software Development Demo | 57:57 | |
| 2 | 002 Context Engineering & Frictionless Setup | 01:07:48 | |
| 3 | 003 Optional Part 1 Office Hours | 01:03:23 | |
| 4 | 004 Part 1 Homework - Live Practice Session | 01:12:52 | |
| 5 | 005 Zero to plan.md using voice transcription | 16:54 | |
| 6 | 006 Working Incrementally with AI Todos and Git | 26:53 | |
| 7 | 007 Adding Tools via MCP | 08:46 | |
| 8 | 008 Week 1 Recorded Breakdowns | 11:42 | |
| 9 | 009 Sandboxed Filesystem for AI Coding with Codespaces | 08:35 | |
| 10 | 010 Interactive Agents & Spec‑First Planning | 01:06:13 | |
| 11 | 011 AI Code Review, PR Orchestration & Security | 57:53 | |
| 12 | 012 Optional Part 2 Office Hours | 01:15:23 | |
| 13 | 013 Optional Part 2 Homework - Live Practice Session | 01:09:07 | |
| 14 | 014 Optional Workshop Investigating and Fixing a Complex Bug with AI Tooling | 01:30:00 | |
| 15 | 015 Defining Good Code for LLM Context | 19:14 | |
| 16 | 016 Using an Agent To Identify Tech Debt | 16:39 | |
| 17 | 017 LLMs in the CLI using the unix philosophy | 16:37 | |
| 18 | 018 AsyncBackground Agents & Dynamic Context | 01:05:12 | |
| 19 | 019 Parallelization, Measuring Efficacy, & Continuous Improvement | 01:03:12 | |
| 20 | 020 Optional Part 3 Office Hourse | 01:02:58 | |
| 21 | 021 Optional Part 3 Homework - Live Practice Session | 01:03:48 | |
| 22 | 022 RepoPrompt - How it helps build context | 48:20 | |
| 23 | 023 SpecStory, SpecFlow, and Spec-Driven Dev | 59:20 | |
| 24 | 024 How I Learned to Stop Worrying and Love AI Agents | 59:02 |
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Frequently asked questions
What prerequisites should I have before enrolling in this course?
To get the most out of this course, you should have a basic understanding of software development and familiarity with coding practices. While the course provides a comprehensive guide to configuring AI tools, prior experience with AI or machine learning is not strictly necessary. Basic knowledge of version control systems like Git and software development environments will be beneficial, as these are referenced throughout the course.
What projects or skills will I develop in this course?
Throughout the course, you'll learn to configure and adapt AI tools like Cursor, Copilot, and Claude Code to suit specific coding projects. The course includes exercises such as developing spec-driven applications and managing incremental workflows with AI Todos and Git. You will also gain skills in using AI for code reviews, PR orchestration, and identifying technical debt, all of which are practical skills applicable in various software development scenarios.
Who is the target audience for this course?
This course is designed for software developers and engineers interested in leveraging AI tools to enhance their coding practices. It is particularly beneficial for those looking to integrate AI assistants into their development workflow to improve efficiency and code quality. The course is also suitable for technical leads and project managers who want to understand how AI can be used for planning and improving development processes.
How does this course compare in depth and scope to other AI coding courses?
Unlike other AI coding courses that may focus solely on tool usage, this course emphasizes adapting AI tools to fit individual coding styles and project contexts. It covers a wide range of topics from context engineering to spec-driven development and includes optional live practice sessions and workshops. This makes it a practical choice for those looking to deeply integrate AI into their coding workflows rather than just learning tool functionalities.
What specific AI tools are covered in this course?
The course covers a variety of AI tools including Cursor, Copilot, Amp, Claude Code, and Windsurf. It also teaches how to use a sandboxed filesystem with Codespaces, and how to employ interactive agents for spec-first planning. Lessons focus on configuring these tools to enhance your coding process and adapting them to your specific development needs.
What topics are not covered in this course?
This course does not cover foundational AI and machine learning concepts in depth, as it assumes a basic understanding of these areas. It also does not focus on the development of AI algorithms from scratch or delve into the detailed workings of machine learning models, as the emphasis is on using pre-existing AI tools to assist in coding tasks.
What is the expected time commitment for completing this course?
The course comprises 24 lessons, including optional office hours and live practice sessions. While the total runtime is not specified, students should allocate enough time for both the structured lessons and additional practice. Given the optional components, students can tailor their engagement level based on personal learning goals, potentially extending the time commitment if they choose to participate in all optional activities.