Module 1: Introduction to Prompt Engineering
Welcome to Prompt Engineering!
In this first module, we are going to learn what prompt engineering means and why it is important when working with big AI language models like ChatGPT, GPT-3, GPT-4, etc.
Prerequisite:
What is a Prompt?
A prompt is just a piece of text or a question that you give to an AI language model. The model will try to complete it or give an answer based on that prompt.
What is Prompt Engineering?
Prompt engineering means writing your prompt in a smart way so that the model gives the best and most accurate output.
You can think of it like this:
“If you ask the right question, you will get the right answer.”
So, prompt engineering is like the art (and science) of talking to AI in a way it understands better.
It helps the AI give meaningful, useful, and more correct responses just by changing how we ask. It is especially important for powerful models like GPT-3/4, which can perform a wide range of tasks depending on how they’re prompted
Example
Example 1:
Prompt: “Translate this sentence into Hindi: I teach machine learning.”
Output: “Main machine learning padhata hoon.”
Example 2:
Prompt: “What is the sentiment of this review? ‘The movie was incredibly boring and a complete waste of time.’”
Output: “Negative”
Good vs Bad Prompt Example
BAD: Summarize the following.
GOOD: Summarize the following scientific article in 2-3 sentences using layman language.
Why Prompt Engineering Matters
- Improves output quality and relevance
- Reduces need for retraining or fine-tuning
- Empowers non-technical users to interact with AI effectively
Prompt Engineering vs Fine-Tuning
| Prompt Engineering | Fine-Tuning |
|---|---|
| No change to model weights | Adjusts model parameters |
| Fast and low-cost | Requires training resources |
| Easily deployed | Needs careful dataset curation |