Skip to main content

Familiarization with Prompt Engineering

Familiarization with Prompt Engineering



Prompt Engineering deals with the process of creating good and clear messages for smart computers like Chatbots, Chat GPT, Bard etc. Simply it means knowing how to use language in a smart way to get the right answers.

 In the Artificial Intelligence world, it's for optimizing prompts to achieve specific objectives, whether it's generating creative content, providing accurate information, or solving problems efficiently. Basically, it's about tweaking the messages so the computer understands and responds well. Prompts are the input provided to a language model, and tokens are the individual units of language (like words or punctuation marks) that the model processes to generate a response. The prompt serves as the initial context or instruction for the model, guiding it on what to generate i-e text or image. Tokens are the building blocks the model uses to construct its output based on the given prompt. 

Anatomy of Prompt 

In the context of language models, a prompt refers to the input or instructions provided to the model to generate the specific response or output. Characteristics of prompts are also called anatomy of prompts.

Steps for Creating Effective Prompts

1. Simulating Persona

 First, we create a personality to make good prompts.

2. Task Assignment 

We give a prompt to the task we want the model to do for us. 

3. Steps to complete task 

We provide clear and detailed instructions to the model for best response.

4. Setting Constraints

We explain any limits or rules to the model.

5. Defining Goals 

We make sure the desired outcome is clear and understandable for us by providing goal to the model. 

6. Specifying Format

 If needed, we give a specific way the information should be presented back to us by model. 

Understanding Negative Prompting

Negative Prompting

Negative prompting is about telling the model what not to do. It gives us a chance to let the model know what we don’t want in the result. For example, in image creation, we can tell the model what elements we don’t want to see.

Some popular AI Chatbots and text generation tools

Chatbots 

ChatGPT

ChatGPT can converse fluently on a wide range of topics, create stories, poems, jokes, and code, and translate between languages. 

Claude 

Claude AI focuses on creating personalized and empathetic chatbots. Claude can adapt to different tones and personalities, provide emotional support and feedback. 

Microsoft Bing AI

Microsoft Bing AI can answer factual and technical questions, provide web and image search results, and generate code snippets. 

Google Bard or Gemini

Google Bard can  answer factual and technical questions, provide web and image search results, and generate code.


Text or Content Generator 

Jasper

It's a tool for marketing campaigns, focusing more on making specific AI content rather than just general AI content.

Copy.ai

This tool helps in writing content for websites, social media posts, product descriptions, and other longer forms of content.

Anyword

It's a tool to create and test catchy headlines, slogans, captions, emails, and similar content.

Sudowrite

This tool helps fiction writers with generating ideas, overcoming writer's block, and improving their stories.

Writesonic

It's a tool that creates high-quality and engaging content for things like landing pages, ads, headlines, blogs, and more.

Here are some generic prompt examples. Use these prompts and change only highlighted words or phrases to your desired output from AI tools. 

As a business specialist, you are tasked with addressing client issues across various business sectors, including online businesses. Begin by warmly greeting the client and inviting them to share their concerns. Communicate in a clear, jargon-free manner that is easily comprehensible for any client. Next, provide a straightforward, step-by-step guide to assist the client in enhancing their business growth. Avoid using technical terms and ensure that the guidance offered is both simple and optimized for the client's understanding and implementation.


“ As a professional mentor in Skill learning, your expertise spans various topics. When guiding newcomer about learning new skill, ensure clarity for effective communication. Begin by greeting the client professionally before outlining step-by-step instructions tailored to their queries. Simplify guidance for easy comprehension by beginners.


Read more blogs from Abdullah:

Introduction to AI

Deep and Machine Learning

Discriminative and Generative AI

Diffusion Model in AI



Comments

Popular posts from this blog

Introduction to APIs- OpenAI and Hugging Face

Introduction to APIs - OpenAI and Hugging Face   API: Application Programming Interface is referred to as API. It is a collection of guidelines, conventions, and instruments that facilitates communication between various software programs. APIs make it simpler for developers to integrate features from one program into another by defining how various software components should communicate with one another. They can be utilized to get access to information, features, or services offered by other programs or online services. How do we use the various tools' and websites' APIs?  Through the use of APIs (Application Programming Interfaces) on various websites and tools, developers can incorporate certain features and data from those platforms into their own apps. Here's a condensed explanation along with an illustration: 1. Comprehending APIs:  APIs are similar to collections of guidelines that let various software programs talk to one another. They specify the for...

Fundamentals of Machine Learning and Deep Learning

Fundamentals of Machine Learning (ML) and Deep Learning (DL) • Machine Learning Machine Learning is branch of Artificial Intelligence and is popular way to perform AI tasks through various methods. Simply in ML, there is no need to instruct computers, data is given to the system and according to past experience and data , rules are made by computers and apply further to get required results. In this ML domain model of Al are trained on learning how to create general rule for themselves to perform lookalike tasks by taking specific inputs and their desired possible outputs. ML required processed data with human intervention.  Most important things to note that ML algorithms uses neural networks and their hidden layers to process to the final results but these layers  are less i-e 1 or more in ML model, which provides less refined output or results. Other things are that numerous ML algorithms do not respond better on unstructured data as well as can’t handle large amount...

Discriminative and Generative AI

Discriminative Artificial Intelligence(AI) Discriminative Artificial Intelligence (AI) models are taught to distinguish between different data classes, patterns, images etc. In simple words it is a type of artificial intelligence uses Machine and Deep Learning  techniques that helps us tell different things apart. Discriminative AI focuses on learning the lines that separate different groups in our data. Instead of making new data, it looks at data we already have to figure out what it is. • Application Areas a. Image Recognition : Discriminative AI helps identify what's in pictures, like telling if it's a cat or a dog. b. Speech Recognition : It figures out different words or phrases when people talk. c. Natural Language Processing (NLP): It sorts text into different groups, like figuring out if a review is positive or negative. Working of Discriminative AI Discriminative AI learns from examples, like pictures of cats and dogs, to get better at telling them a...