From Research Question to AI Experiment: A GitHub Project That Structures Your Work

Ever had a messy Jupyter notebook full of promising AI experiments, only to realize a week later you can't remember which hyperparameter change led to that one good result? Or maybe you're starting a new research project and the sheer chaos of managing prompts, model outputs, and evaluations feels overwhelming before you've even begun.

That's the exact problem the AI-PhD-S26 project tackles. It's a GitHub repository that provides a structured framework for turning loose, exploratory AI research questions into organized, reproducible experiments. Think of it as a lightweight lab notebook for the modern AI developer.

What It Does

AI-PhD-S26 is essentially a template and a set of practices for systematic AI experimentation. It moves you away from ad-hoc scripts and towards a consistent project structure. The core idea is to separate your experimental components: your prompts (or input data), your model calls (to various APIs or local models), and your evaluation logic. By keeping these pieces modular, you can easily run controlled experiments, compare results, and track what actually worked.

The repository provides a clear directory layout and suggests a workflow where you define your research questions, iterate on your prompts systematically, run batches of experiments, and then analyze the outputs in a structured way. It's less about providing new tools and more about enforcing a sensible, reproducible methodology using the tools you likely already have.

Why It's Cool

The real value here isn't in groundbreaking code; it's in the enforced discipline. For solo developers or small teams diving into prompt engineering, fine-tuning, or model evaluation, this structure prevents the common "spaghetti experiment" pitfall.

A clever aspect is how it normalizes the experimentation loop. By treating a prompt as a configurable variable and model calls as a separate service, you can easily A/B test different prompt versions against multiple models (like GPT-4, Claude, or open-source alternatives) without rewriting your core logic. This makes your findings concrete and shareable. You're not just saying "Prompt B seemed better"; you can show the exact setup and results that led to that conclusion.

It's particularly useful for use cases like:

• Systematically improving a prompt for a specific task.
• Comparing the performance of different LLMs on your custom benchmark.
• Building a reproducible pipeline for generating and evaluating synthetic data.

How to Try It

The best way to get started is to use the repository as a template. Head over to the AI-PhD-S26 GitHub repo and click the "Use this template" button to create your own copy under your account. This gives you the full directory structure to start with.

From there, it's about adapting the pattern to your stack. The repository's README outlines the philosophy and structure. You'll set up your own configuration files for API keys, write your experiment scripts following the separation of concerns (data, model, evaluation), and start logging your results. It's a framework, so you fill in the details with your specific models and tasks.

Final Thoughts

As AI development becomes more experimental, having a robust methodology is as important as the code itself. This project offers a pragmatic, low-overhead way to get that structure. It won't do the research for you, but it will make sure your work is understandable—to your future self and to others. If you've ever lost a week's progress to poor experiment tracking, giving this structured approach a shot might save you a lot of future headaches. It's a simple idea, but sometimes that's exactly what makes a project genuinely useful.

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