Applying the 6 steps of the INSPIRe framework to speed up your code generation (ChatGPT-4 — Claude 3 — Gemini)
I’ve never written a Data Science project from start to complete. Yet anything you possibly can do inside a Jupyter Notebook I can do too. Yes, really, anything.
Bragging aside, that is some of the significant transformations AI brought us within the last two years. LLMs turned code right into a commodity; something you should use as a tool as a substitute of a skill.
All you wish are five ingredients:
- Data literacy.
- A pinch of logic.
- A knack for trial and error.
- Prompt Engineering.
- The INSPIRe framework.
Should you’re wondering what the heck INSPIRe could be, you must read the primary a part of this series. You’ll find an introduction to the framework and find out how to apply it. Should you’re feeling adventurous, nevertheless, the fast recap in the next section should suffice.
In this text, we’ll dive right into a concrete example of the INSPIRe framework. We’ll construct an artificial data generator that produces fictional reviews.
Why synthetic data? Why fictional reviews?
Synthetic data generation plays a key role in training and fine-tuning LLMs — and LLMs are some of the sought-after technologies in today’s market. These models excel at analyzing and interpreting product reviews and social media comments, capabilities highly valued by your clients.
Touching on each topics is like lighting two candles with one match.
I’ve argued Prompt Engineering is the most popular programming language of 2024. That’s mainly because LLMs will let you write code in plain English.
Coding in natural language is the cornerstone of the INSPIRe framework.