Prompt engineering is an advanced field but to start with, you should know the basic concept of prompts. They should understand what are AI prompts and how LLMs like ChatGPT and Gemini by Google interpret the prompts to deliver a specific output to the users. The main uses of AI prompts are for:
Content generation: AI prompts are used for various text formats like blogs, articles, scripts, code, etc.
Translation: Translating text between languages, for eg translate Russian to Spanish, translate English to Kiswahili, etc.
Summarization: AI prompts are used to condense the lengthy text into concise summaries. For example, you input a 1000-word blog in the AI model and command it to produce a 50-word summary of the blog.
Question answering: All AI models like Gemini and ChatGPT provide answers to a user’s direct question. The question could be anything – from a science subject question like ‘What is the relationship between the resistance of a wire and its length?’ to something you usually ask a search engine like ‘How does high-quality human content writing help in search engine rankings?’
Creative writing: It includes asking the model to generate poems, landing pages, stories, or scripts.
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What are the top 4 methods for prompt engineering?
If you pursue a course in prompt engineering, mastering 4S will be one of the key aspects of it. The field is still evolving but based on research and work in this area so far, we can answer what are the 4 S's of prompt engineering. The field functions on 4 main ‘S’. They are:
1. Zero-shot learning
In zero-shot learning, engineers provide an AI model with a task without examples. The language model relies solely on pre-trained knowledge to deliver the best output.
An example of zero-shot learning would be a single command like ‘‘write a video script for developing technology for colonizing Mars.’’
2. Few-shot learning
In the second ‘S’ of AI prompt engineering called few-shot learning, you provide the AI model with a few examples. The purpose behind this is to give a better understanding of the task it is to perform to provide the output.
An AI prompt engineering example of few-shot learning would be ''Write a video script for developing powerful spacecraft propulsion systems that enable long-duration space travel. Example 1: A documentary-style script explaining the challenges of current propulsion systems. Example 2: An interview with a leading space propulsion engineer.''
3. Chain-of-thought prompting
In this 3rd ‘S’ of prompt engineering, you break down a complex input into more comprehensible and intermediate steps. The idea here is to guide the AI model to reason through these smaller steps to offer the best solution in the form of the output.
Here is a prompt engineering example of a chain of thought. Which is the best seafood dish to try in a restaurant? Think: What types of seafood are popular? What cooking methods are delicious for seafood? What criteria define a great seafood dish (taste, presentation, freshness)? Based on these factors, which seafood dish would be the best choice?''
4. System prompting
In system prompting, you provide general instructions to the AI before the actual task. This will set the context for the model to provide the desired output.
This is an example of system prompting. "You are an informative career assistant in the field of AI training jobs. Please provide comprehensive details on what an AI training job is."
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