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• Prompt engineering is an emerging field that requires creativity and attention to detail. It involves selecting the right words, phrases, symbols, and formats that guide the model in generating high-quality and relevant texts.

• Prompt engineers are experts who write prose — rather than code — to test AI chatbots. Their job is to identify the AI's errors and hidden capabilities so developers can address them.

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  Goldman Sachs recently published a report which suggests that approximately 18% of jobs around the world may be automated by generative artificial intelligence (AI), potentially affecting up to 300 million jobs. So in a scenario like this, prompt engineering is a skill that will shape the future of technology.

The prompt is a few words or longer piece of text designed to get a specific response from the model.

Prompts are used in applications like chatbots (like chatGPT, Bard and Bing Chat etc..), Q&A Systems and Language translation systems.

Examples of AI-Powered Chatbots:

• Openai's chatGPT

• Google's Bard

• Microsoft's Bing Chat (free)

Prompt engineering is a concept in artificial intelligence, particularly natural language processing (NLP).

What does a Prompt Engineer do?

A Prompt Engineer is a professional who specializes in "developing, refining and optimizing AI-generated text prompts to ensure they are accurate, engaging and relevant for various applications".

Example: Asking ChatGPT! to get a response is a PROMPT.

Scenario:

Here, we prompt (ask) ChatGPT in 2 ways about the same topic, let's take the topic as our "prompt engineering"

The first way(regular way) of asking a question to ChatGPT about Prompt Engineering (you can take any topic).

ME: Hi, please easily explain prompt engineering.

Answer from ChatGPT:

Prompt engineering refers to the process of designing and refining prompts for language models like ChatGPT to achieve desired responses. It involves crafting instructions or queries in a way that guides the model's output toward a specific goal or style.

In prompt engineering, the goal is to provide clear and explicit instructions to the model to generate responses that align with the desired outcome. This can involve various techniques, such as:

1. Instruction modification: Adjust the wording or structure of the prompt to make the desired outcome more explicit. By providing specific details, context, or constraints, you can guide the model to generate more accurate or tailored responses.

2. System message: Including a system message at the beginning of the prompt can set the behavior or role of the AI model. For example, specifying that the model is an expert in a particular field can lead to more knowledgeable responses.

3. User message: Framing the user's input or question in a way that guides the model to answer in a specific manner. By structuring the user's message or asking the question differently, you can influence the model's response style or focus.

4. Temperature and length control: Adjusting the "temperature" parameter of the model can control the randomness of the responses. Lower values make the model more focused and deterministic, while higher values introduce more randomness. Similarly, controlling the "max length" parameter limits the length of the generated response.

5. Iterative refinement: Experimenting with different prompts, instructions, or approaches and iteratively refine them based on the model's output. By observing and analyzing the model's responses, you can fine-tune the prompts to improve the quality, relevance, or style of the generated text.

Prompt engineering is an essential technique for shaping the behavior of language models, enabling users to obtain more accurate and desirable outputs. It requires an understanding of the model's capabilities and limitations, as well as creative thinking in designing prompts that elicit the desired responses.

The second way (the right way) of asking a question to ChatGPT about Prompt Engineering(any topic).

ME: You are an expert in prompt engineering, and you have 10 years of experience in teaching prompt engineering to children. I want you to be my teacher for today and explain things like I am 10 years old. make sure to provide some funny examples to understand easily, is that OK?

Answer from ChatGPT:

Of course! I'll be happy to be your prompt engineering teacher for today and explain things in a way that's easy for a 10-year-old to understand. I'll do my best to make it fun and provide some funny examples. Let's get started!

Imagine you have a magic talking robot, and you want to tell it something so it gives you a specific answer. That's what prompt engineering is all about—giving clear instructions to the robot to get the response you want.

Here are some funny examples to help you understand:

1. Let's say you have a robot that can make sandwiches, but sometimes it puts strange things together. To make sure you get a good sandwich, you could say, "Hey robot, make me a delicious sandwich with yummy peanut butter and jelly, but please don't add pickles or spaghetti!" By giving these specific instructions, you help the robot understand your preferences and avoid any weird combinations.

2. Imagine you have a robot pet that loves to play catch but sometimes gets too excited and knocks things over. To make sure it plays safely, you could say, "Hey robot pet, let's play catch with a softball in the backyard, but remember, no throwing it too hard, or we'll have to clean up broken things!"

3. Now, let's say you have a robot friend who knows a lot about animals, but sometimes it gives you silly or made-up facts. To get accurate information, you could say, "Hey robot friend, tell me an interesting fact about penguins. Make sure it's a real fact, not something silly like 'Penguins love to dance the hula!'"

4. Finally, imagine you have a robot that can draw pictures, but sometimes it gets carried away and makes silly drawings. If you want it to draw a specific object, you could say, "Hey drawing robot, please create a beautiful picture of a cute puppy playing with a ball. Don't make the puppy wear sunglasses or give it three tails!"

Remember, when you give clear and specific instructions to your robot friends, they can understand what you want and give you better results. Prompt engineering is like being the boss of the robots and telling them exactly what to do to make you happy.

I hope these funny examples helped you understand prompt engineering! If you have any more questions, feel free to ask.

by observing the above two scenarios, the responses from ChatGPT have a lot of differences, right?

So the best way of communicating with AI models is to be just like as we communicate with the people around us.

{{Prompt Hacks}}

Example Prompt :1:

Category:

Q&A

Purpose:

Answer questions based on existing knowledge.

Prompt:

Result:

• C: Context

• S: Specific Information

• I: Intent/Goal

• R: Response Format

Example:

Context: "I'm a software developer"
Specific Information: "working on a Python project"
Intent/Goal: "Can you explain how to implement exception handling in Python?"
Response Format (if needed): Write it in a simple paragraph or list.

Result:

Perfect Prompt: "I'm a software developer working on a Python project. Can you explain how to implement exception handling in Python? Write it in a simple paragraph or list.

Source:

In prompt engineering, there are several useful tools and techniques that can help you craft effective and specific prompts for language models like ChatGPT. Here are some of the best tools used in prompt engineering:

1. OpenAI Cookbook's "ChatGPT Prompts Guide": OpenAI Cookbook provides a comprehensive guide on using prompts with ChatGPT. It covers various techniques and examples to create prompts that yield desired responses. The guide offers insights into question-answering, instruction following, conversation continuation, and more.

2. InstructGPT Playground: The InstructGPT Playground is a tool developed by OpenAI that allows you to experiment with different prompts and instructions to generate detailed responses. It provides a user-friendly interface to interact with the model and observe its behavior based on the prompts you provide.

3. Human Evaluation: While not a tool in the traditional sense, human evaluation is a crucial component of prompt engineering. It involves gathering feedback from human evaluators to assess the quality and appropriateness of model responses generated by different prompts. This feedback helps fine-tune prompts and improve model performance.

4. Rule-based Prompting: Rule-based prompting involves crafting prompts that explicitly instruct the model to follow specific guidelines or constraints. By providing clear instructions and examples, you can guide the model's behavior and encourage it to generate desired responses. Rule-based prompting is often combined with reinforcement learning from human feedback to iteratively improve prompts.

5. Prefix Engineering: Prefix engineering involves adding a standardized prefix to every prompt to guide the model's behavior consistently. The prefix can include instructions, context, or any other information that sets the desired tone or style for the conversation. By defining the prefix carefully, you can influence the model's behavior throughout the conversation.

6. Randomness Control: Language models like ChatGPT have a tendency to be excessively verbose or repetitive. You can control the level of randomness in the responses by adjusting the temperature parameter during inference. Lower values like 0.2 make the model more focused and deterministic, while higher values like 0.8 introduce more randomness.

7. Prompt Engineering Libraries: Various libraries and frameworks have been developed to facilitate prompt engineering with language models. For example, the gpt-3.5-turbo package by OpenAI provides an API that allows you to interact with ChatGPT programmatically, giving you more control over prompts and responses.

Remember, prompt engineering is an iterative process that involves experimenting with different techniques, evaluating the results, and refining the prompts based on the desired outcomes.

In prompt engineering, there are several frameworks and modules that can assist you in crafting effective prompts and interacting with language models. Here are some of the best frameworks and modules used in prompt engineering:

1. OpenAI API: OpenAI provides an API that allows you to interact with language models like ChatGPT programmatically. The API enables you to send prompts and receive model-generated responses, giving you flexibility in prompt engineering. You can use the OpenAI API to integrate language models into your own applications or scripts.

2. Hugging Face Transformers: The Hugging Face Transformers library is a popular open-source library for natural language processing (NLP) tasks. It provides a wide range of pre-trained models, including GPT-3.5 and other variants. Transformers simplify the process of interacting with language models, allowing you to generate responses based on prompts and fine-tune models using custom datasets.

3. GPT-3.5 Turbo Python Package: OpenAI's gpt-3.5-turbo Python package is specifically designed for prompt engineering with ChatGPT. It offers a convenient way to interact with the ChatGPT model, enabling you to send messages and receive model responses in a conversational manner. The package provides higher-level abstractions for prompt engineering tasks.

4. OpenAI Cookbook's Prompting Tools: The OpenAI Cookbook is a community-driven resource that provides practical examples and guides for working with language models. It offers a range of prompt engineering tools and techniques, including rule-based prompting, example-based prompting, and more. The Cookbook's recipes and code snippets can help you get started with prompt engineering effectively.

5. GPT-3 Sandbox: GPT-3 Sandbox is an open-source Python library that simplifies the process of interacting with the GPT-3 model from OpenAI. It provides a high-level API to send prompts and receive responses, handling the underlying API calls and result processing for you. GPT-3 Sandbox can be a useful tool for quick prototyping and experimentation with prompt engineering.

6. Prompting Utilities: Prompting utilities refer to custom scripts or modules developed by the prompt engineering community to facilitate specific prompt-related tasks. These utilities can include functions or classes for processing prompts, generating prefixes, tokenizing text, or applying post-processing to model responses. They often leverage existing NLP libraries and frameworks to streamline prompt engineering workflows.

These frameworks and modules provide different levels of abstraction and functionality, catering to various prompt engineering needs. Depending on your specific use case and programming language preference, you can choose the framework or module that best suits your requirements and integrates well with your existing codebase.

additional notes will be added soon, till then,

Keep Practicing and Keep Smiling.

Thank you

Yours Loving Dev

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