AI in development - Skeleton structure #247

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adamblanchard merged 13 commits into main from using-ai-in-development-proposal

Jan 13, 2026

shared-modules/using-ai-in-development/README.md

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     By the end of this module, you will be able to:
     - [ ] Use AI to support your development tasks such as refactoring and reviewing code.
-    - [ ] Evaluate AI-generated code for correctness, expected outcomes, security and maintainability.
     - [ ] Explore prompting techniques to understand complex code, code snippets and tough technical concepts.
-    - [ ] Use AI to supporting learning new programming languages, frameworks and tools.
+    - [ ] Evaluate AI-generated code for correctness, expected outcomes, security and maintainability.
+    - [ ] Use AI to support learning new programming languages, frameworks and tools.
     - [ ] Identify and integrate AI into appropriate parts of the software development workflow.
     - [ ] Describe ethical, legal and risk considerations of using AI in professional software development.
     - [ ] Explore the possible futures of how AI is changing the role and expectations of developers in the industry.
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shared-modules/using-ai-in-development/week1/session-plan.md

            
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    # Session Plan

    TODO This should be written and planned around the learning goals. Take a look at the foundation module to see what trainees should already know.

    The idea behind this session is to teach how AI can be used in the day-to-day development workflows of a developer.

    Some notes from previous conversations, that could be used as inspiration/included somehow:

    Start by giving a brief overview of a usual workflow, and then dive into specific parts of it and how AI can help there, with specific prompts and exercises to practice. The suggestion is that mentors talk through specific use cases, and demo the prompts in the session. The exercises are designed for the trainees to practice themselves on their own machines.

    - [Our current proposal for hyf AI usage expectations (in pr form)](https://github.com/HackYourFuture-CPH/programme/pull/118/files)

    - [Suggestions from @te-online](https://github.com/HackYourFuture-CPH/programme/pull/118#issuecomment-3070705560)

    - [Github has a whole bunch of public training content for copilot, which we could heavily base ours on](https://skills.github.com/#take-flight-with-github-copilot)

    ## AI in the development workflow

    ### Workflow overview

    0. Personal development - Reading, learning, practicing new software development skills

    1. Problem definition - Understanding what we're trying to solve

    2. Planning - Deciding what to build

    3. Design - Deciding how it should work and look

    4. Implementation - Building the software

    5. Testing - Ensuring it all works correctly

    6. Code review - Getting feedback and finalising the code

    7. Deployment - Shipping it to users

    8. Monitoring - Making sure things keep working as intended

    After the overview, go through some specific phases to highlight key ways AI can support them. Of course there are many possiblilities in every phase (and we should encourage trainees to explore them themselves!), but the below are mostly aimed to inspire and give some concrete examples in a few of the phases that trainees can start using today.

    ### Personal development - Learning

    Outside of HYF or work, spending time learning and practicing new development skills is important. This could be learning new programming languages, frameworks, tools or concepts.

    Here's some examples:

    #### 1. "Explaining by comparison" prompt

    Learn quickly by mapping new knowledge to things you already understand.

    ```prompt

    I know how to do X in <this language I already know>:

    <paste code snippet here>

    Show me how to write the same functionality in <new language> and explain:

    1. What concepts are the same

    2. What's different and why

    3. What would surprise a developer coming from <language I already know>

    ```

    #### 2. The "Code Tutor" prompt

    Learn a new language/framework/tool through practice, and getting feedback to improve.

    ```prompt

    I'm learning how to do X in <language/framework/tool>. Here is my current solution:

    <paste code snippet here>

    Review this as if I were a junior developer:

    1. Point out any issues, bugs or bad practices

    2. Suggest better practices or patterns

    3. Explain what each change means and why it matters

    4. Create a mini summary of what I should study next, based on my weaknesses and mistakes

    ```

    #### 3. The "Learning plan" prompt

    If you have a big topic ahead of you, get help to create a learning path to help you stay on track and avoid becoming overwhelemed.

    ```prompt

    I want to learn X as a developer who already knows Y and Z. Create a 4 week self-study plan focussed on hands-on coding practice.

    For each week, include:

    1. Key concepts to learn

    2. One small practical project

    3. Common mistakes and misunderstandings beginners make

    4. How this new thing differs from what I already know

    ```

    #### Exericse 1

    Use each of the three prompts above using your own personal learning goals, and evaluate what you like and don't like about the output of the AI. Bonus: Try tweaking a prompt template to shape the output more to your own personal needs.

    {% hint style="info" %}

    **Top tip**

    If AI writes code you can’t explain:

    - Slow down

    - Ask why

    - Rewrite it yourself until you understand

    That is the moment where learning happens, so don't miss out by skipping ahead!

    {% endhint %}

    ### Design - Code explanation

    While preparing ideas for a particular solution in the Design phase, you'll likely come across the challenge of needing to understand existing code.

    TODO add some prompting examples here, as well as good follow up prompts

    #### Exercise 2

    Use AI to help explain the code in [Exercise 2](TODO create and insert link here). Ask follow up questions until you have a good grasp of what every function and line of code is achieving.

    TODO write the exercise code, inspired by [exercise](https://github.com/hosseinzahed/github-copilot-use-cases/blob/main/01-code-explanation.py)

    ### Implementation - Learning new approaches

    While writing code, you may come across a roadblock where you're not entirely sure how to implement something.

    TODO add some prompting examples here

    #### Exercise 3

    Use AI to give you some suggestions on possible solutions to [Exercise 3](TODO create and insert link here).

    TODO write the exercise and supporting code

    ### Implementation - Code refactoring

    After you write a solution, you may wonder if there's a neater or better way to structure the code.

    TODO add some prompting examples here

    #### Exercise 4

    Use AI to help you refactor the code in [Exercise 4](TODO create and insert link here).

    TODO write the exercise code, inspired by [exercise](https://github.com/hosseinzahed/github-copilot-use-cases/blob/main/03-code-refactoring.ts)

    ### Implementation - Bug fixing

    Before your solution is finished, you'll need to make sure it's bug free.

    TODO add some prompting examples here

    #### Exercise 5

    Use AI to help you uncover the bug in the [Exercise 5](TODO create and insert link here) code, understand why it's happening, and fix it.

    TODO write the exercise code, inspired by [exercise](https://github.com/hosseinzahed/github-copilot-use-cases/blob/main/04-bug-fixing.py)

    ### Implementation - Documentation generation

    AI is not only useful in generating code, but also documentation.

    TODO add some prompting examples here

    #### Exercise 6

    Use AI to draw a diagram to explain how the code in [Exercise 6](TODO create and insert link here) works. Confirm that it is correct, and fix any mistakes.

    TODO write the exercise code, inspired by [exercise](https://github.com/hosseinzahed/github-copilot-use-cases/blob/main/05-document-generation.py)

    ### Code review - Feedback assistance

    When your code is ready, it will be time for getting feedback from other developers. Before you do that, save some time and get some initial feedback from AI on improving your code.

    TODO add some prompting examples here

    #### Exercise 7

    Use AI to get some structured feedback on improving the code in [Exercise 7](TODO create and insert link here), and make the changes.

    TODO write the exercise and supporting code

    ### Agent mode

    TODO explain a little what agent mode is and how it differs to "edit" mode. Maybe warn that it will use up a lot more of your usage credits. Reminder of our [AI usage guidelines](https://program.hackyourfuture.dk/guidelines/ai-usage), and for HYF assignments and projects

    #### Exercise 8

    TODO this should be a small exercise to show how the agent flow works. Could be something similar to [this](https://github.com/hosseinzahed/github-copilot-use-cases/blob/main/19-copilot-agent.md). Suggest instead that trainees explore using agent mode further on their own personal projects we do not allow generating full coding solutions.

    ## AI in the workplace

    ### Ethics, legal and risk considerations

    TODO these follow on from the foundation content, but should be give more software dev specific examples, like the risk of pasting protected IP code or customer data into third party AI tools.

    ### The future of AI in development

    We don't know the future for sure, that's what makes it exciting. There are some trends that we are seeing:

    #### Trends

    1. Less time typing code

    2. The gap between junior and mid-level narrows

    3. More cross-functional roles

    4. Understanding the "why"

    5. Super fast industry shifts

    #### Tips

    1. Stay in the loop of AI developments

       - Follow key organisations in the space (OpenAI, Anthropic, Microsoft/Github, Google/Gemini)

       - Join in conversations with other developers online and offline to see what they're experiencing

       - Follow AI related news

    2. Learn and practice with new tools/features

       - Try out new AI features and capabilities as they are released

       - Use AI tools in your real projects to practice

    3. Integrate useful parts into your own workflow

       - Whatever you find works well, integrate it into your own daily working practices

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