26 prompt ChatGPT +50% di qualità ✅

Silvio Luchetti

11 Mar 202415:51

Summary

TLDRThe video script discusses the concept of prompt engineering and design for large language models like GPT, Gemini, and Lama. It highlights a study that identifies 26 techniques and tips for crafting better prompts leading to improved responses. The techniques range from being direct and avoiding negative language to incorporating audience knowledge and dividing complex tasks into simpler instructions. The video emphasizes the importance of clear objectives, examples, and structured prompts to activate the latent memory of the model for accurate and detailed answers.

Takeaways

🤖 The effectiveness of large language models like GPT, Gemini, and Lama depends on the quality of the prompts used to interact with them.
🔍 Prompt engineering and design are crucial fields of study to optimize interactions with AI models.
📈 A well-conducted study identified 26 techniques or tips to create better prompts leading to improved responses from AI.
🎯 Directness in prompts can lead to more concise responses from language models, avoiding polite phrases like 'please' or 'thank you'.
🧐 Tailoring prompts to the expected audience, such as assuming the reader is an expert or a novice, can significantly alter the response.
📝 Breaking down complex tasks into a sequence of simpler instructions can improve the AI's performance, especially in problem-solving.
🤔 Using 'chain of thoughts' techniques can help in guiding the AI through a step-by-step process, which is beneficial for complex problem-solving.
🚫 Avoiding negative directives and using affirmative language can help maintain clarity and avoid confusion in the AI's responses.
💡 Incorporate examples and use a 'few-shot' approach to train the AI on what is expected from it, providing practical examples to guide its output.
📋 Structuring prompts with clear sections for instructions, examples, questions, and context can enhance the AI's understanding and performance.
📝 Asking the AI to continue a text using specific words, phrases, or sentences provided can help maintain coherence and style.
🔧 For complex coding tasks, prompts can be structured to generate code across multiple files and create scripts for automating file creation or modifications.

Q & A

What is the main focus of the video?
-The main focus of the video is to discuss the concept of prompt engineering and provide 26 techniques or tips for crafting better prompts that lead to improved responses from large language models like GPT, Gemini, and Lama.
Why is asking the right question important when interacting with large language models?
-Asking the right question is crucial because it directly influences the quality of the responses from large language models. Properly structured questions can lead to more accurate, relevant, and concise answers by guiding the model's interpretation and focus.
What is the first principle suggested in the video for prompt engineering?
-The first principle suggested is to be direct in the questions. There is no need to be overly polite when interacting with large language models, so phrases like 'please' and 'thank you' are not necessary; simply state the question or request concisely.
How can incorporating the audience into the prompt improve responses?
-Incorporating the audience into the prompt helps tailor the response to the expected reader's knowledge level. For example, if the audience is an expert, the prompt should reflect that and avoid basic explanations, whereas if the audience is a novice, the information should be presented in a more simplified manner.
What is the technique of breaking down complex activities into simpler instructions?
-The technique of breaking down complex activities into simpler instructions involves dividing a complex task into a sequence of easier steps. This method helps the language model to better understand and execute the task by following each step individually, which can lead to more accurate and actionable responses.
How can using examples in prompts enhance the responses from language models?
-Using examples in prompts provides clear guidelines and expectations for the language model, which can improve the relevance and accuracy of the responses. It helps the model understand the desired output format and the specific information to include.
What is the purpose of incorporating phrases like 'You will be penalized' in prompts?
-Incorporating phrases like 'You will be penalized' is a way to guide the language model towards providing higher quality responses. It encourages the model to search its latent memory for better information rather than providing the first thing that comes to mind.
What is the 'chain of thought' technique mentioned in the video?
-The 'chain of thought' technique involves structuring the prompt in a way that it guides the language model through a logical sequence of steps or reasoning. This can help the model to provide more detailed and step-by-step explanations, enhancing the clarity and depth of the response.
How can the 'few-shot learning' method be applied in prompt engineering?
-The 'few-shot learning' method in prompt engineering involves providing a small number of examples or instances before the main prompt. This helps the language model to understand the context and the type of information expected in the response, leading to more accurate and relevant outputs.
What is the significance of using clear and precise requirements in prompts?
-Using clear and precise requirements in prompts helps the language model to understand exactly what is expected, reducing the chances of misinterpretation and ensuring that the response is tailored to the specific needs of the user. It also encourages the model to provide more detailed and targeted information.
What is the advice given in the video for improving the language model's understanding of a prompt?
-The advice given is to use sectioning techniques, such as creating distinct sections for instructions, questions, and examples, and to use placeholders and markers that the language model can recognize. This helps the model to differentiate between different parts of the prompt and to focus on the relevant information for each section.

Outlines

00:00

🤖 Understanding Prompt Engineering

This paragraph introduces the concept of prompt engineering and prompt design, emphasizing the importance of asking the right questions to elicit better responses from large language models like GPT, Gemini, and Lama. It mentions a study that identified 26 techniques and tips for crafting better prompts, leading to improved responses. The discussion highlights the need to be direct in queries and to avoid unnecessary politeness, as well as the idea of tailoring prompts to the model's simulated latent memory.

05:00

📚 Techniques for Effective Prompts

The paragraph delves into specific techniques for creating effective prompts. It discusses strategies such as integrating the target audience's expertise into the prompt, breaking down complex tasks into simpler instructions, and using 'chain of thoughts' to guide the model through a series of logical steps. The paragraph also touches on the use of examples and the importance of clarity and simplicity in explanations, as well as the concept of 'prompt engineering' in teaching the model to perform tasks through examples and structured instructions.

10:02

🎯 Advanced Prompting Techniques

This section explores more advanced prompting techniques, such as using 'few-shot' learning to provide examples and guide the model towards the desired output. It also introduces the concept of 'prompt-based learning' where the model is trained with practical examples to understand what is expected. The paragraph discusses the use of 'primers' at the end of prompts to imply the beginning of the desired output and the importance of maintaining an impartial response without relying on stereotypes or biases.

15:03

📈 Practical Applications and Feedback

The final paragraph shifts focus to the practical applications of the discussed techniques and encourages viewers to provide feedback on whether they found the information useful. It invites viewers to request more videos on the topic and to subscribe to the channel for practical tips on leveraging artificial intelligence to save time and focus on what truly matters. The speaker emphasizes the importance of humans focusing on their unique intelligence rather than automating trivial tasks.

Mindmap

Keywords

💡Large Language Models

Large Language Models, such as GPT, Gemini, and Lama, are advanced AI systems designed to understand and generate human-like text. They are capable of responding to various prompts and questions, but their performance can vary depending on how the questions are framed. In the video, the speaker discusses techniques to optimize these models' responses through prompt engineering, highlighting the importance of asking the right questions to elicit better answers.

💡Prompt Engineering

Prompt engineering is the practice of crafting input text (prompts) for AI models in a way that encourages the desired output. It involves understanding how the AI model processes information and structuring the prompts to guide the AI towards specific responses. The video emphasizes the significance of prompt engineering in improving the quality of answers from Large Language Models.

💡Directness

Directness in the context of the video refers to the straightforward and concise manner of posing questions to Large Language Models. It suggests that using polite or roundabout language is not necessary and may even hinder the AI's performance. Being direct helps the AI model to simulate the latent memory part more effectively, leading to more accurate responses.

💡Audience Integration

Audience integration is the strategy of tailoring prompts to fit the expected audience's knowledge level. This technique involves considering who will receive the information and adjusting the prompt accordingly, ensuring that the AI's response is appropriate and understandable for the target audience.

💡Chain of Thoughts

Chain of Thoughts is a prompt engineering technique that involves breaking down complex tasks or problems into a sequence of simpler instructions or steps. This method helps the AI model to process the information in a more structured manner, leading to a clearer and more logical response.

💡Positive Directives

Positive directives are affirmative and constructive instructions given to the AI model to avoid negative language and focus on the desired outcome. This approach helps the AI to generate responses that are more aligned with the user's intentions and goals.

💡Simplicity

Simplicity in the context of the video refers to the practice of explaining complex concepts or ideas in easy-to-understand terms. It involves using clear and straightforward language that can be comprehended by a wide range of audiences, including those with limited knowledge of the subject matter.

💡Incentivization

Incentivization is the strategy of motivating the AI model to provide better responses by offering a reward or a positive outcome for achieving a specific goal. This technique can encourage the AI to search its latent memory for higher quality information or solutions.

💡Example-Based Prompts

Example-based prompts are prompts that include specific examples to guide the AI model towards the desired output. By providing examples, the user helps the AI understand the context and the type of response expected, leading to more accurate and relevant answers.

💡Sectioning

Sectioning is the process of dividing the prompt into distinct sections, such as instructions, examples, questions, and context, to organize the information and make it easier for the AI model to process. This technique can improve the clarity and structure of the AI's response.

💡Impartiality

Impartiality refers to the need for AI-generated responses to be unbiased and free from stereotypes or preconceived notions. It is important to ensure that the AI's answers are based on objective information and do not reflect any form of bias.

💡Clarity and Precision

Clarity and precision are essential for effective communication, especially when interacting with AI models. They involve expressing ideas or questions in a clear, understandable manner and providing enough detail to ensure that the AI model can generate accurate and relevant responses.

Highlights

The importance of prompt engineering and prompt design in eliciting better responses from large language models like GPT, Gemini, and Lama.

A study found 26 techniques and tips for crafting better prompts leading to improved responses.

Directness in prompts can lead to more concise responses from language models.

Incorporating the expected audience into the prompt can lead to tailored and relevant responses.

Breaking down complex tasks into simpler instructions can enhance the model's performance.

Using affirmative directives can improve the clarity and effectiveness of prompts.

Explaining concepts in simple terms or as if to a child can yield more understandable responses.

The use of examples in prompts can guide the model towards the desired output.

Incorporate specific phrases to guide the model towards a particular objective or response.

Penalizing certain responses can encourage the model to search for better answers in its latent memory.

Natural and human-like phrasing in prompts can elicit more engaging and relatable responses.

Techniques like 'chain of thoughts' can help in structuring the prompt for logical problem-solving.

Using section markers in prompts can help the model understand and compartmentalize information effectively.

Assigning a role to the language model can enhance the specificity and relevance of its responses.

Utilizing output primers can shape the beginning of the model's response to meet certain expectations.

The importance of avoiding bias and stereotypes in the model's responses for impartiality.

Encouraging the model to ask questions until sufficient information is gathered for the desired output.

The application of these techniques can significantly improve the efficiency and effectiveness of using large language models.