Craft. Command. Create. The Power of Prompts
Craft. Command. Create. The Power of of Prompts
Question: What is Prompt Writing in Generative AI?
Answer: Simply put, a prompt is how you ask ChatGPT, Gemini, Bing Image Creator or Copilot to do something for you
Definition: Prompt writing in generative AI involves crafting input prompts to guide an AI model to produce outputs you desire.
This is essential in getting the best results from language models. The goal is to structure the input in a way that maximizes the model's ability to generate useful, accurate, and relevant responses.
You need to structure an instruction that can be interpreted and understood by a generative AI model.
Essentially, it’s about asking the right question to get the best output from the model.
Recall the 1980’s GIGO? Garbage In, Garbage Out! The same principle applies here.
To reiterate, Generative AI prompts are instructions or questions?you use to tell Generative AI what you want.
Prompts can include four parts: the goal, context, expectations, and source.
Goal: What do you want from ChatGPT, Gemini or Copilot?
(Example: for my presentation on Emotional Intelligence, I want a list of topics to prepare the PowerPoint presentation)
Context: Why do you need it and who are involved?
(Example: For the upcoming sales review meeting where I want to help the medical representatives and the first-line managers to understand more about Emotional Intelligence. This will help them in developing superior relations with Key Opinion Leaders in their Areas.)
Expectations: How should ChatGPT, Gemini or Copilot respond to best fulfill my request?
(Example: I want very simple language which can be understood by all. There should be no technical jargon which can confuse all).
Source: What information or samples do you want ChatGPT, Gemini or Copilot to use?
(Example: I want information from Daniel Goleman, Paul Ekman distilled to suit my audience of medical representatives and first-line managers. ???
Purpose: It enables direct interaction with the model using plain language prompts. In the past, working with machine learning models required deep knowledge of datasets, statistics, and modeling techniques.
Now, you can “program” language models like ChatGPT, Gemini, Bing Image Creator or DALL-E in English or other languages.
Types of Prompts
Direct Prompts (Zero-shot): Simplest type—provides only the instruction without examples.
Prompting with Examples (One-, Few-, and Multi-shot): Include examples alongside the instruction.
"Direct Prompts" and "Zero-shot Prompts" are terms used to describe specific ways of interacting with Generative AI models, particularly in the context of Natural Language Processing (NLP) and text generation. Here's a detailed explanation.
Direct Prompts
Direct Prompts refer to straightforward, unambiguous questions or commands given to a Generative AI model. The goal is to elicit a specific response without requiring additional context or clarification. This type of prompt is designed to minimize ambiguity and guide the AI toward providing the desired information or performing a specific task.
Examples of Direct Prompts
- What is the capital of India?
- Translate 'Hello' to French.
- "Summarize the main points of the MAHABHARAT."
In these examples, the questions are clear and direct, making it easier for the AI model to understand and respond accurately.
Zero-shot Prompts
Zero-shot Prompts involve asking the AI model to perform a task or answer a question without providing it with prior examples or training on that specific task. The model relies on its pre-existing knowledge and generalization capabilities to generate a response.
This is called "zero-shot" because the model has received zero specific examples of the task at hand.
Examples of Zero-shot Prompts:
- "Generate a short story about a rabbit and a tortoise."
- "Explain the concept of quantum computing."
- "What would be a good slogan for a new eco-friendly product?"
In these examples, the AI model uses its training data and understanding of language to produce an answer, even though it hasn't been specifically trained on these tasks.
Key Differences
- Direct Prompts are often specific and clear, intended to elicit a precise response from the model.
- Zero-shot Prompts require the model to generalize from its training data to perform a task it hasn't explicitly been trained on.
Applications and Benefits
Direct Prompts:
- Useful for obtaining specific information quickly.
- Ideal for applications like customer support, information retrieval, and simple task execution.
Zero-shot Prompts
- Showcase the model's ability to generalize and handle a wide range of tasks.
- Useful in scenarios where creating task-specific training data is impractical or impossible.
- Demonstrate the flexibility and broad applicability of the model.
Examples in Context
Direct Prompt
You: "What is the capital of Bangladesh?"
AI: "The capital of Japan is Dhaka."
Zero-shot Prompt
You: "Write a poem about the Indian Ocean."
AI: “The Indian Ocean, vast and grand, A sapphire cradle, kissed by land. From Africa's rise to Asian shores, its restless heart forever roars. "
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In the zero-shot example, the AI generates a creative output based on its understanding of poetry and the concept of the ocean, without needing specific prior examples of ocean-themed poems.
Both types of prompts are essential tools for leveraging the capabilities of Generative AI models, each serving different purposes and demonstrating different strengths of the AI.
Prompting with Examples
Prompting with Examples, a technique in prompt engineering, helps guide large language models towards the desired response. Here's how it works:
Imagine you're teaching a child addition. You wouldn't just say "solve for x: 2 + 3." Instead, you'd show examples: "1 + 1 = 2" and "2 + 2 = 4." Similarly, in prompting, you provide the model with example inputs and their corresponding outputs for the task at hand. This way, the model learns the pattern and can apply it to new, unseen prompts.
For instance, you could give a model examples of question-answer pairs about history to prime it for answering your questions on a specific historical event. This approach refines the model's understanding and steers it towards giving you the kind of response you're looking for.
Difficulty Level:
Prompt engineering doesn’t require coding experience. While it’s not rocket science, creativity and persistence are key. As LLMs (large language models) and prompt engineering evolve, anyone can learn and improve their skills.
Modifying Prompt Scripts:
You can modify your prompt script as often as needed. Experiment, iterate, and refine based on the model’s responses and your desired outcomes. There’s no strict limit on how often you can change it.
Tips for Scripting Prompts
Here are some great and simple tips to help you script effective prompts.
Be Clear and Specific: Clearly state what you want the AI to do. Vague prompts can lead to ambiguous responses.
Instead of "Tell me about electric cars," try "Explain the advantages of electric cars over petrol and diesel cars."
Provide Context: Giving context helps the model understand the background and provide more relevant responses.
Instead of "Write a story," try "Write a short story set in a dystopian future where Big Brother Robot will rule the world."
Use Examples: Showing examples of the desired output can guide the model effectively.
For instance, "Generate a creative tagline for a new eco-friendly lamitube for a moisturizer. Example: 'Moisturizer - Hydrates green, live clean.'"
Specify Format: If you need the output in a specific format, mention it.
"List three benefits of waking up at BramhaMuhurtam in bullet points.
Iterate and Refine: Based on the outputs you get, tweak and refine your prompts for better results.
If the response isn't quite right, adjust your prompt to be more detailed or focused.
Limit Scope: For complex tasks, break them down into smaller, more manageable parts.
Instead of "Write a research paper on climate change," try "Summarize the main causes of climate change in 200 words."
Difficulty Level of Learning Prompt Engineering
Prompt engineering isn't as complex as rocket science, but it does require some understanding of how AI models interpret and respond to text. It's more about practice and understanding the nuances of language and the specific AI model's behavior. Here are some factors that contribute to the learning curve:
1.??? Understanding the Model: Knowing how the model works, its strengths, and its limitations.
2.??? Experimentation: Trying different prompts to see what works best.
3.??? Feedback Loop: Continuously refining prompts based on the outputs you get.
While it can be challenging at first, it becomes easier with experience and practice.
My personal experience
When I need high intellectual levels of output, through experimentation, trials and errors, I have learned that ChatGPT is most suitable. For instance if you want to know why an iPhone gives you the best images, I will use ChatGPT.
However, if I want help to a create headline or a tagline for the advertisement of the latest version of iPhone advertisement, I will prefer Gemini (earlier Bard).
Similarly, for copyediting an article or a post or even a book, I would rely more on Gemini.
And for getting help in tracking historical information, which can help you write and create stories, I found Copilot the most suitable LLM.
Frequency of Changing or Modifying Your Prompt Script
You can change or modify your prompt script as often as needed. Here are some guidelines on when to do so:
1. Immediate Need: If the output isn't as expected or is irrelevant.
2. Experimentation: Regularly try different prompts to see if you can get better results.
3. Feedback Integration: Modify prompts based on feedback from users or stakeholders.
4. Adaptation to Context: Adjust prompts to better suit the specific context or audience,
Wish our teachers, professors, bosses or even our parents are like Gemini I asked Gemini: “If I am not happy with your responses and I keep on modifying my prompts again and again, will it not irritate you or make you angry?” Gemini responded” “As a large language model, I don't get emotions like irritation or anger. My purpose is to assist you, and that includes iterating on prompts until you get the desired results. In fact, I find it helpful when you refine your prompts! It shows you're engaged in the process and helps me learn how to better respond to your needs. The more you provide feedback on my responses, the better I can adjust my approach and provide you with the information or creative text formats you're looking for. So, don't hesitate to modify your prompts as much as you need. We can work together to get the best possible outcome!”
To conclude, you have powerful language models, but you need a way to tell it what to do. That's where prompts come in.
Prompts are instructions, in natural language, that guide generative AI models towards specific tasks. They can be simple questions, like "Write a poem about a cat," or more complex, including context and desired styles.
Here's where prompt engineering takes the wheel. It's the art of crafting effective prompts to unlock the full potential of these models. This involves several key aspects:
By mastering prompt engineering, you can leverage generative AI for various tasks: writing different kinds of creative content, generating code snippets, or even summarizing complex topics. Remember, a well-crafted prompt is the key to unlocking the true power of generative AI.
Postscript
Do you know who poses the greatest challenge to OpenAI's ChatGPT?
Anthropic!
Anthropic, a formidable competitor launched its latest AI model, Claude 3.5, approximately three months ago.
The company asserts that this new offering surpasses its rivals, including OpenAI's ChatGPT, Google's Gemini, and Microsoft's Copilot in performance.
While all four are AI language models designed to assist users, they differ significantly in terms of creators, training methodologies, and capabilities. However, making direct comparisons of their relative performance is challenging due to the lack of comprehensive, standardized data.
The suitability of these AI models for specific topics or use cases is analogous to a doctor prescribing an antibiotic. The doctor who prescribes the antibiotic is aware which is best suited for a particular medical condition. This is through usage and the doctors experience.
Similarly, only through user experience you will learn which is most suitable for a specific subject and context. User experience will ultimately determine which model is most appropriate for a given task.
It is advisable to remain open to exploring multiple options, as each model may excel in different areas.
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A Strategic Thinker & Leader over 3 decades in Industry & Academia| PhD in Pharmaceutical Mgmt (OTC Drugs)| Program Head -Pharma MBA| Qualified Digital Marketer | Distinguished Professor| Consultant | Start Up Advisor
4 个月Wow A useful tool to help AI work better & ensure relevant outputs !