(This story is part of a series where we attempt to explain the building blocks of quite an old field of software engineering, that has gained great popularity today, i.e. Generative AI)

In this story, we start with the basics of Prompt Engineering in Generative AI, including the definition, elements, and a few use cases of the same.

What is Prompt Engineering?

Prompt Engineering is the process of writing prompts for artificial intelligence language models. Effective prompt engineering combines natural language processing (NLP), machine learning, and cognitive psychology to design and optimize text prompts that produce desired and optimal responses from AI language models (generally LLMs). Prompt engineering is a means to create accurate, informative, and engaging responses. This is achieved by carefully crafting the input prompts.

Prompt engineering has gained importance in recent times due to the emergence and high popularity of advanced Large Language Models (LLMs) like OpenAI’s GPT, Google’s BERT, Meta’s Llama, etc., and their over-reaching use-cases in the present-day world. Prompt engineering helps make effective use of advanced LLMs for real-world applications.

As the world is swiftly adopting Applied (Assistive) AI, though Prompt Engineering as a profession might seem like a new kid on the block; however it is definitely playing an important role in the kind of software engineering jobs the technology industry (is and) will create in days to come.

Elements of Prompt

1. Instruction

It is a clear directive/command of what to do

Example of a prompt instruction

2. Question

It is a query whose answer is expected as the output

Example of a prompt question

3. Input/Context

It is data that is provided along with the instruction or the question, that helps steer the model to give/generate relevant output/response to the instruction/question.

In the following prompt, “This is a happy world” is the Input

4. Input Data | Examples

There can be zero to multiple examples that can be provided as part of the prompt. Examples help to get a better, more relevant, and curated output.

5. Output Indicator/Format

This specifies the format in which the final output is to be presented.

UseCases | Applications

With each passing day, as the world is getting exposed to Generative AI and its capabilities, the applications of prompt engineering are rising exponentially.

To list a few

1. Content Generation

Generative AI and effective prompt engineering has opened new avenues for leveraging AI and rich data all over the internet, to generate content in all formats (text, image, audio, video, etc.). The generated content can help create engaging user experiences for people using technology platforms.

The applications of AI-assisted content generation are limitless, a few examples being:

Educational e-platforms successfully use Generative AI to help create exams and quizzes for the students
Auto-completing email clients
AI-assisted art & literature generation

2. Summarization

AI-assisted summary generation of lengthy data (eg: lengthy documents, long videos/audio, etc.), will potentially help make optimum use of time in the coming days. Summarization will potentially find many use-cases in academic research, business development, healthcare, and manufacturing.

3. Translation

AI-assisted language translation has been around for quite a while now. However, with recent advancements in Generative AI, the translation techniques have become more accurate and real-time.

4. Assisted Software Engineering

Assisted software engineering using tools like Github Copilot, Code GPT, etc. have shown quite promising results, and are helping automate certain software engineering aspects of technology.

Image Source — Nira

5. Assisted Support

Chatbots have been around for a while now. However, Generative AI has improved the quality of such chatbots and virtual assistants by miles in a very short amount of time. The ability to understand context, and give relevant responses has improved exponentially. The ability to give relatable personas to these virtual assistants has become possible and is getting better with effective prompt engineering.

References

In the subsequent story, we will deep dive into the internal working of prompts, and try to understand what it takes to build an effective prompt engineering framework, and related jargon around prompt engineering.

What is Prompt Engineering? — Part 1: The Basics