Delve into the practical tips and tricks for AI development using large language models (LLMs). Learn how to utilize visual prompts, screenshot iteration, context reference, and efficient debugging techniques to streamline your programming projects.
1. Introduction
• Overview of AI programming journey
• Introduction to key tools and projects developed
2. Omni Prompting
• Explanation of visual prompts
• Example of using visual prompts to create a React component
3. Screenshot Iteration
• Concept of screenshot iteration
• Example of modifying a project using screenshot iteration
4. Context Reference
• Importance of referencing specific files
• Example of integrating front-end and back-end components
5. Effective Debugging
• Approach to debugging in AI projects
• Example of debugging a common error with detailed context
6. Conclusion
• Recap of tips and their benefits
• Encouragement to explore further resources and tipsIntroduction
Over the last few months, I’ve immersed myself in AI programming, leveraging large language models (LLMs) to build various projects. From creating a website to developing a SaaS product connected to Stripe and even designing an iOS app for processing budgets, I’ve gathered valuable insights and techniques I want to share with you. These tips and tricks have significantly streamlined my development process, and I hope they can do the same for you.
Omni Prompting
One of the most powerful techniques I’ve discovered is using visual prompts called “Omni Prompting.” Instead of relying solely on text-based instructions, I start by sketching my ideas visually or finding an example in a design site like dribbble. For example, when designing a screen for an app, I use a simple drawing tool like SketchPad.app to outline the layout. Once I have a sketch, I download a png or take a screenshot and paste it into my AI tool, along with a detailed prompt describing the desired features.
For instance, if I want an iOS SwiftUI screen with a title, menu navigation, text input, upload buttons, a submit button, and a video player, I include this information along with the screenshot in my prompt. This approach allows the AI to generate code that closely matches my visual design, complete with step-by-step instructions for setting up the project. The result is an iOS screen that looks exactly like my initial sketch, making it easy to bring my app ideas to life.
Screenshot Iteration
Another technique that has proven incredibly useful is “Screenshot Iteration.” This method involves iteratively refining my projects by using screenshots to convey changes. Suppose I want to move buttons to a new position on a webpage. I take a screenshot of the current layout, draw annotations to indicate the changes and paste the annotated screenshot back into the AI tool with a prompt describing the modifications. Working with Claude 3.5, Projects, and Artefacts makes this iterative process much easier – but I’ve done it successfully with ChatGPT 4o before Claude’s update – so the technique works with either LLM.
For example, if I want to center buttons under a text box, I annotate the screenshot with arrows and text instructions. The AI then generates updated code based on my visual input. This iterative process allows me to implement changes quickly and accurately, ensuring that the final layout meets my specifications.
Context Reference
Referencing specific files and contexts can significantly enhance the AI’s understanding and output when developing complex projects. I learned this technique while building my app using an LLM. I can provide the AI with a comprehensive context by uploading relevant files from both the front-end and back-end components of my project. Even though Claude keeps versions of the files, I’ve found it helpful to give back updated versions to the LLM, so it has your updated editions.
For instance, I upload files like View.swift, DetailedView.swift, and backend files like main.py. By referencing these files in my prompts, I can help the LLM better connect the front-end and back-end seamlessly, creating cohesive and functional components. This approach ensures that the AI understands the project’s structure and can generate code that integrates smoothly with existing elements.
Effective Debugging
Debugging is a crucial part of any development process, and using LLMs can simplify this task significantly. When encountering an error, I gather as much context as possible to help the AI diagnose and fix the issue. This involves taking screenshots of error messages, copying console logs, and providing detailed descriptions of the problem.
For example, if I introduce an error using an incorrect model name in my code, I document the error message and related console logs. I then paste this information into the AI tool, asking for assistance resolving the issue. The AI can analyze the provided context and suggest changes to fix the problem. This method not only speeds up the debugging process but also improves the solutions’ accuracy.
Conclusion
These tips and tricks for using LLMs in AI development have transformed my programming approach. By leveraging visual prompts, screenshot iteration, context references, and detailed debugging techniques, I’ve streamlined my workflow and achieved better results, improving my overall productivity. I encourage you to explore these methods in your projects, discover their benefits, and become a 10X Programmer with AI – an AI Code Titan!
