Prompt Engineering 101 - Crash Course & Tips
TLDRIn this informative video, Patrick from Assembly AI introduces viewers to the fundamentals of prompt engineering, a critical skill for effectively utilizing large language models. The video covers the five key elements of a prompt: instructions, questions, examples, context, and desired output format. Patrick explains various use cases such as summarization, classification, translation, text generation, question answering, coaching, and even image generation. He provides practical tips for crafting clear, concise, and effective prompts, including the use of examples, specifying output formats, and aligning prompt instructions with desired tasks. The video also delves into specific prompting techniques like length and tone control, audience and context control, and Chain of Thought prompting. Patrick shares valuable hacks to improve output quality, such as allowing the model to think before responding and breaking down complex tasks. He emphasizes the importance of iteration in finding the best prompts and concludes with a list of resources for further learning. The video is a comprehensive guide for anyone looking to enhance their interactions with language models and achieve better results.
Takeaways
- π **Prompt Elements**: A good prompt includes at least one instruction or question and may also contain input/context, examples, and desired output format.
- π **Use Cases**: Prompts are versatile and can be used for summarization, classification, translation, text generation, question answering, coaching, and even image generation with some models.
- π‘ **General Tips**: Be clear and concise, provide relevant context, use examples, specify output format, and align instructions with tasks to improve results.
- π§ **Chain of Thought Prompting**: For complex questions, provide a step-by-step thought process to guide the model to the correct answer.
- π« **Avoid Hallucination**: Instruct the model to use reliable sources and avoid making up information to ensure factual responses.
- π **Control Output**: Apply techniques like length, tone, style, audience, and context control to direct the model's output.
- π **Iterating Tips**: Finding the best prompt is an iterative process; try different prompts, rephrase instructions, and adjust the number of examples provided.
- π€ **Persona Use**: Utilize different personas to achieve specific voices or styles in the model's responses.
- π **Example Inclusion**: Including examples, known as few-shot learning, can help the model understand the desired output format.
- π **Length Control**: Specify the desired length of the output to manage the verbosity of the model's responses.
- π **Scenario-Based Guiding**: Set the scene or scenario to align the model's responses with the desired context or conversational tone.
- β **Subtask Breakdown**: Break down complex tasks into smaller, manageable steps to guide the model through the process.
Q & A
What is the main focus of the video by Patrick from Assembly AI?
-The video focuses on the basics of prompt engineering to help users get the best results when working with large language models.
What are the five elements of a prompt as mentioned in the video?
-The five elements of a prompt are input or context, instructions, questions, examples, and a desired output format.
What is the significance of including at least one instruction or question in a prompt?
-Including at least one instruction or question in a prompt ensures that the model understands the task and can provide a relevant response.
What is the term for providing an example within a prompt?
-Providing an example within a prompt is also known as view shot learning. If only one example is used, it's called One-Shot learning.
What are some common use cases for prompts with large language models?
-Common use cases include summarization, classification, translation, text generation or completion, question answering, coaching, and with some models, even image generation.
What is the purpose of specifying the desired output format in a prompt?
-Specifying the desired output format helps to increase the chances of receiving the expected type of response from the language model.
How can providing examples in prompts improve the results?
-Providing examples, especially through few short learning, can help the model understand the expected output format and style, potentially improving the accuracy of the response.
What is Chain of Thought prompting and how does it help in complex tasks?
-Chain of Thought prompting is a technique that showcases the process of how the correct answer to a question should be reached. It helps in complex tasks by guiding the model through the logical steps needed to arrive at the answer.
What are some tips to avoid hallucination when using prompts with language models?
-Tips to avoid hallucination include explicitly instructing the model not to make anything up, asking for relevant quotes from the text to back up claims, and using Chain of Thought prompting to guide the model towards a logical conclusion.
How can the model's comprehension be checked in a prompt?
-Comprehension can be checked by including a step in the prompt where the model is asked if it understands the instruction, and then it can confirm its understanding before providing the answer.
What is the importance of iterating when finding the best prompt?
-Iterating is important because it involves trial and error, allowing users to test different prompts, rephrase instructions, and adjust examples to find the most effective way to communicate with the language model and achieve the desired outcome.
What additional resource is mentioned in the video for working with the LIMO framework?
-The video mentions a LIMO best practices guide that provides prompting best practices for those who want to work with the LIMO framework to apply large language models to audio.
Outlines
π Introduction to Prompt Engineering
Patrick from Assembly AI introduces the concept of prompt engineering, emphasizing that it's not just about listing the best prompts but rather understanding the underlying concepts and fundamentals. The video aims to provide a comprehensive guide covering the basic elements of a prompt, use cases, general tips, specific prompting techniques, and resources for further learning. Patrick mentions that prompts can include instructions, questions, examples, and desired output formats, and at least one of these should be present for an effective prompt.
π Elements and Use Cases of Prompts
The video delves into the five elements of a prompt: input or context, instructions, questions, examples, and desired output format. Patrick explains that while not all elements are required, having at least one instruction or question is crucial. He then outlines various use cases for prompts, including summarization, classification, translation, text generation, question answering, coaching, and even image generation with some models. The paragraph also touches on the importance of clarity, conciseness, providing relevant context, and specifying the desired output format to improve prompt effectiveness.
π‘ Prompting Techniques and Tips
Patrick shares a list of guidelines to enhance prompt quality, such as being clear and concise, providing relevant information, including examples, and specifying the output format. He also discusses the importance of encouraging factual responses and aligning prompt instructions with the desired task. The paragraph introduces specific prompting techniques like length control, tone control, style control, audience control, context control, and scenario-based guiding. A key technique highlighted is Chain of Thought prompting, which helps in solving complex questions by providing a step-by-step thought process. The paragraph concludes with tips to avoid hallucination in responses, such as instructing the model not to make things up and to use relevant quotations from the text.
π οΈ Prompting Hacks and Iteration Advice
The video presents several hacks to improve the output of prompts, such as allowing the model to say 'I don't know' to prevent incorrect information, giving the model space to think before responding, breaking down complex tasks, and checking the model's comprehension. Patrick also offers iterating tips, suggesting that finding the best prompt often involves trial and error. He advises trying different prompts, rephrasing instructions, trying different personas, and adjusting the number of examples provided. The paragraph concludes with a summary of the key points and a reference to additional resources for learning more about prompt engineering.
Mindmap
Keywords
π‘Prompt Engineering
π‘Large Language Models (LLMs)
π‘Elements of a Prompt
π‘Use Cases
π‘General Tips
π‘Specific Prompting Techniques
π‘Chain of Thought Prompting
π‘Avoiding Hallucination
π‘Cool Hacks
π‘Iterating Tips
π‘One-Shot Learning
Highlights
Learn the basics of prompt engineering to optimize results with large language models.
A prompt can include five elements: input or context, instructions, questions, examples, and a desired output format.
At least one instruction or question should be present in a good prompt.
Use cases for prompts include summarization, classification, translation, text generation, question answering, coaching, and image generation.
Clear and concise instructions are key to effective prompt engineering.
Providing relevant information or data as context can improve prompt outcomes.
Examples in prompts, known as few-shot learning, can enhance model responses.
Specifying the desired output format increases the chances of getting the desired result.
Encourage the model to be factual to avoid hallucination.
Aligning prompt instructions with tasks can lead to more accurate and relevant responses.
Using different personas can help achieve more specific voices in model responses.
Length, tone, style, audience, and context control are specific prompting techniques to manage output.
Chain of Thought prompting is useful for complex questions and instructions, guiding the model to the correct answer.
Avoid hallucination by instructing the model not to make anything up.
Giving the model room to think before responding can improve accuracy.
Breaking down complex tasks into subtasks can simplify the prompt for the model.
Checking the model's comprehension ensures it understands the instructions before providing an answer.
Iterating on prompts through trial and error is crucial to finding the best possible prompt.
The AssemblyAI Lemur framework offers best practices for applying LLMs to audio.