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• What is the CodePath Applied AI Engineering course?

  CodePath's Applied AI Engineering pathway is designed to help computer science students master software engineering in an AI-native world. Rather than treating AI as a separate topic, the program embeds it directly into programming practice. Students strengthen their understanding of data structures, algorithms, and object-oriented programming while learning to use AI tools as collaborators for debugging, scaffolding, and refinement, always with human oversight and critical evaluation.

• Why should I take this CodePath course?

  This no-cost 10-week course from CodePath will prepare you for how software is actually built today, where AI is always in the loop. You'll learn to code, design systems, and debug in environments that mirror professional practice, gaining the skills employers now expect:

  • • Apply core CS fundamentals using AI as a collaborative tool.

    • Evaluate, refine, and explain AI-generated code confidently.

    • Gain exposure to industry-standard workflows and tools such as GitHub, Copilot, and modern AI-powered IDEs.

    • Develop portfolio-ready projects that showcase your technical reasoning and AI fluency.

    • Strengthen interview-style communication by practicing how to explain design decisions and AI-assisted solutions.

  You'll learn from industry-vetted instructors, collaborate with peers nationwide, and join a network of emerging engineers supported by major tech partners.

  
  

• What does "AI-assisted programming" mean in practice?

  It means that whenever you write, test, or debug code, you will learn to:

  • Use AI to generate or refactor code from specifications

  • Debug with AI while verifying correctness and intent

  • Critique and improve AI outputs

  • Collaborate with AI as you design, document, and test your work

  
  The human is always in charge, but AI is always in the loop.

• How is this different from other AI courses?

  Most AI courses teach AI tools as standalone skills. In this program, AI is integrated into the act of programming itself. You'll learn computer science fundamentals, software engineering practices, and advanced AI concepts in an environment where AI is always present as a collaborator.

• What topics do these courses cover?

  In the foundations course, you'll strengthen your programming foundations while integrating AI into your workflow. Core topics include:

  • Data structures, algorithms, and object-oriented programming in Python.

  • Critical evaluation and refinement of AI-generated code.

  • Algorithmic thinking and prompt engineering.

  • Machine learning literacy (supervised, unsupervised, generative models).

  • Exploratory practice with Retrieval-Augmented Generation (RAG), agentic workflows, lightweight fine-tuning, and AI guardrails.

  • Professional collaboration and version-control workflows using Git and GitHub.

   

  In the advanced course, you'll apply these foundations in real, open source engineering environments:

  • AI-augmented collaboration in large codebases.

  • Spec-driven "vibe coding" and debugging.

  • Integration of advanced AI techniques (RAG, agents, fine-tuning, guardrails) at production level.

  • Professional Git workflow, code reviews, and contributions merged into public repositories.

  • Portfolio-ready deliverables that demonstrate AI-native engineering practices.

• Will I learn machine learning in this program?

  You'll gain literacy in high-level ML concepts – enough to understand what models are, the difference between supervised and unsupervised learning, and why these approaches matter. You won't need advanced math or statistics. The goal is to give you the language and perspective to talk about machine learning intelligently, while focusing your hands-on work on AI-assisted programming.

• Will I get to build projects that include AI as features?

  Yes! In the foundations course, you will design and build projects that integrate AI both as a development partner and as a product feature. Early projects may include chatbots, summarization tools, or automated documentation assistants, and you’ll also gain introductory hands-on exposure to advanced techniques such as retrieval-augmented generation (RAG), agentic workflows, fine-tuning, and guardrails.

  In the advanced course, you'll apply these capabilities at scale in open source projects, producing portfolio-ready contributions that demonstrate your ability to engineer reliable, production-style AI features.

• How will this help me in interviews?

  Employers increasingly ask candidates to interpret, review, and explain AI-generated code. You'll practice these skills alongside debugging, testing, and explaining your reasoning. Combined with your project work and open source contributions, you'll leave with strong preparation for technical interviews.

• Will I get exposure to open source?

  Yes! In the foundations course, you’ll be introduced to open source practices such as GitHub workflows, contribution etiquette, and collaboration conventions.

  As you progress to the advanced course, you'll contribute directly to real-world open source repositories, building professional experience and portfolio-ready artifacts.

• Do I need prior AI experience?

  No. The courses are designed to start with the fundamentals while integrating AI into the process from the beginning. You'll learn to program in an AI-native way without needing prior exposure to AI or ML.

• Do I need any other previous experience?

  You should have completed at least one introductory programming course (in Java, Python, or a similar language). We expect students to come in with basic familiarity in:

  • Writing simple programs using variables, loops, and conditionals
  • Working with arrays or lists
  • Defining and calling functions or methods
  • Understanding core concepts of object-oriented programming (classes, objects, methods)

  We do not assume prior knowledge of advanced topics like data structures, algorithms, or design patterns. Those are taught in the course, directly in AI-assisted workflows. What matters most is that you're comfortable with the basics of programming and ready to think critically about how to refine AI outputs rather than accept them at face value.

• Where can I find the syllabus for the course?

  Here's the link to the syllabus of each course: 

  • Foundations of AI Engineering
  • Applications of AI Engineering
  • AI Open Source Capstone

• How much time will I need to complete the assignments?

  Students can expect to spend up to at least 4-8 hours (depending on level) outside of class completing assignments to get the most value from the class.
  
  Including class hours, students must allocate:

  • AI110: 4-6 hours per week
  • AI201: 7-9 hours per week
  • AI301: 7-9 hours per week
    
    

• Who will the instructors be?

  The course instructors will be professionals from major technology companies, start-ups and academia.

• What if I need additional help during the class?

  CodePath teaching assistants will be available to help with assignments. You will be in a Slack channel where you can ask questions, and you can also email your instructors if you have further questions.

• Will I receive a certificate upon completion of the course?

  Yes, you will receive a certificate of completion upon successful completion of the course.

• Is this class really offered at no-cost? How are you able to do that?

  Yes, CodePath is a nonprofit with backing from major tech companies like Amazon, Google, Meta and Salesforce. Thanks to the generous support of our sponsors, we can offer our classes to any eligible student for no-cost.

• How does CodePath determine who is admitted to the course?

  We believe our students have the richest experience when we have a cohort of students who are focused on their computer science education and motivated to land an internship or job in the tech industry. To that end, we take a wide array of factors into our admissions decisions including, but not limited to:

  • Performance on our HackerRank Readiness assessment
  • Eligibility criteria (see above section)

• When will I hear back about the status of my application?

  We process applications on an ongoing basis and generally release decisions within one week of HackerRank completion. All Summer 2026 admissions decisions will be made by May 25, 2026.
  
  If you have not heard back from us, email admissions@codepath.org and our team will get back to you.

• How can I get more information about this course?

  Please email admissions@codepath.org with any questions.