In this article, you will learn how to use Google Colab’s AI-assisted coding features, specifically AI prompt cells, to generate, explain, and improve Python code directly in a notebook environment.
Topics covered include:
- How AI prompt cells work in Colab and where to find them
- Practical workflow for generating code and safely running it in executable code cells
- Key limitations to keep in mind and when to use the “magic wand” Gemini panel instead
Let’s continue.
Setting up a working Google Colab AI-assisted coding environment
Image by editor
introduction
This article will focus on: Google collaborationis an increasingly popular, free, and easily accessible cloud-based Python environment suitable for prototyping data analysis workflows and experimental code before moving it to production systems.
Based on the latest version of Google Colab, which is available for free at the time of this writing, we employ a step-by-step tutorial style to explore how to effectively use recently introduced AI-assisted coding features. Yes: Colab has built-in tools for AI-assisted coding, including code generation from natural language, written code explanation, autocomplete, and smart troubleshooting.
Explore Colab’s AI-assisted capabilities
First, sign in to Google Colab with your Google account of choice and click (New Notebook) to start a new coding workspace. Good news. This all happens in the cloud and all you need is a web browser (ideally Chrome). You don’t need to install anything locally.
There’s a big novelty here. Anyone familiar with Colab will be familiar with its two basic cell types. One is a code cell for writing and running code. and text cells. Supplement your code with explanations, explanations, and even embedded visuals that explain what’s happening in your code. Now, there is a third type of cell, which is not clearly identified at first glance. its name is AI prompt cell.
This is an all-new special cell type that supports direct one-shot interaction with Google’s most powerful generative AI models in the Gemini family, making it especially useful for those with limited coding knowledge.
Creating an AI prompt cell is easy. In the top toolbar, just below the menu, click the small drop-down arrow next to (Code) and select (Add AI Prompt Cell). You should still see something like this in your blank notebook.
Create AI prompt cells to generate code from natural language
Try writing the following in the “Ask me anything…” text box. Write Python code that generates 100 values for five different types of weather forecast values and plots a histogram of these values.
Be patient for a few seconds, even if nothing seems to happen at first. AI is processing your requests behind the scenes. Ultimately, you may receive a response similar to the following from your selected Gemini model.
Leverage AI prompt cells and executable code cells
This new feature provides a comfortable fit. AI-assisted coding Not just an ideal environment, For code generation,but also, Rapid prototyping, explore new ideas,or Make existing code more understandableFor example, by instructing the AI to insert explainable functions or informative print statements in relevant parts of the program. Understanding the functionality of this new cell type is key to successfully leveraging Colab’s latest AI-assisted coding capabilities.
A standard code cell directly below each AI prompt cell provides practical symbiosis. why? This is because the output of an AI prompt cell is not directly executable code, but often includes text explanations before and after the code. Try copying the code part of the response and pasting it into the code cell below.
Sometimes everything doesn’t work as expected? No problem. of AI prompt cell stays therein a dedicated location in the notebook, so You can continue to interact and refine your code until it fully meets your requirements..
However, it should be noted that this newly introduced cell type has some limitations. Regardless of where the AI prompt cell is in the notebook, the AI prompt cell does not automatically recognize the contents of the rest of the notebook. To ask something about an AI prompt cell, you must provide code to the AI prompt cell.
For example, suppose you placed previously generated code into several code cells for step-by-step execution. Next, add another AI prompt cell at the bottom of the notebook and ask the following question:
AI Prompt AI response when requesting code outside of cell
Notice the response. The AI asks you to explicitly provide (paste) the code you want to analyze, explain, etc., regardless of where it resides in your notebook. You also can’t refer to cells by identifiers like #7 or #16 or ask questions like “”.Rewrite the third code cell in a more concise Python style”.
Here we outline a best practice workflow that we recommend that you become familiar with.
- Add AI prompt cells immediately after cells (or small groups of cells) where you expect a lot of analysis, refinement, and code changes.
- Paste the target code and use explicit instructions that include action verbs such as “Explain,” “Refactor,” “Simplify,” and “Add error handling.”
- Manually review and run the results in carefully placed backup code cells according to your data conversion workflow (you may need to place them before or after the cell containing the original code).
AI prompt cells are perfect for comfortable coding experiments in the main playgroundbut keep in mind the following: For other AI-assisted tasks, such as explaining or converting code in cells, click the magic wand icon Available in the code cell is what allows you to open the Gemini tab on the right side of Colab to continue the interaction. Still the best and most flexible approach.
summary
Google Colab continually releases new AI-assisted coding capabilities that have clear advantages but also come with important limitations. In this article, we reviewed the latest features with a special focus on one of the newest features, AI Prompt Cells, and explained how to get the most out of it and when you should rely on other features for tasks like explaining or refactoring existing code.
