Prompt Engineering 101 - Crash Course & Tips

AssemblyAI
24 Jun 202314:00

TLDRIn this video, Patrick from Assembly AI introduces the fundamentals of prompt engineering, crucial for optimizing interactions with large language models. The video covers the key elements of a prompt, including instructions, questions, examples, and desired output formats. It explores various use cases like summarization, translation, and question answering, and offers practical tips for crafting effective prompts. Patrick also shares specific prompting techniques, such as length and tone control, and provides hacks to enhance output quality. The video concludes with iterating tips to refine prompts for better results.

Takeaways

  • 😀 Prompt Engineering is essential for optimizing interactions with large language models.
  • 🔍 The video provides a comprehensive guide to the basics of prompt engineering, not just a list of prompts.
  • 📝 A good prompt can include elements like input, instructions, questions, examples, and desired output format.
  • 💡 Clear instructions or questions are crucial for effective prompts, and examples can enhance the model's understanding.
  • 🌟 Use cases for prompts include summarization, classification, translation, text generation, question answering, and coaching.
  • ✅ Tips for better prompts include being clear and concise, providing relevant context, and specifying the desired output format.
  • 🧠 Chain of Thought prompting is a technique for complex tasks, guiding the model through a logical process to reach an answer.
  • 🚫 Avoiding 'hallucination' in the model's responses is important, achieved by encouraging factual responses and avoiding made-up information.
  • 🛠️ Specific prompting techniques can control the output, such as length, tone, style, audience, context, and scenario-based guiding.
  • 🔄 Iterating on prompts is key, as finding the best prompt often requires trial and error, and adjusting based on results.

Q & A

  • What is the main focus of the video by Patrick from Assembly AI?

    -The main focus of the video is to teach the basics of prompt engineering to get the best results when working with large language models.

  • What are the five elements of a prompt according to the video?

    -The five elements of a prompt are input or context, instructions, questions, examples, and 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 including an example in a prompt, and what are the different types of example-based learning mentioned in the video?

    -The term for including an example in a prompt is 'view shot learning'. The different types of example-based learning mentioned are One-Shot learning (one example) and Few-Shot learning (more than one example).

  • What are some common use cases for prompts with large language models as discussed in the video?

    -Some common use cases for prompts with large language models include summarization, classification, translation, text generation or completion, question answering, coaching, and image generation.

  • What are the general tips provided in the video to improve prompt engineering?

    -General tips to improve prompt engineering include being clear and concise, providing relevant context, using examples, specifying the desired output format, encouraging factual responses, aligning prompt instructions with tasks, and trying different personas.

  • What is Chain of Thought prompting, and how can it be used to control the output?

    -Chain of Thought prompting is a technique where a series of logical steps are provided to showcase how the correct answer to a question should be reached, helping the model to understand and follow the reasoning process.

  • How can you prevent hallucinations in the model's responses according to the video?

    -To prevent hallucinations, you can explicitly instruct the model not to make anything up, ask it to only use reliable sources, or select relevant quotations from the text to back up its claims.

  • What are some cool hacks mentioned in the video to improve the output of prompts?

    -Some cool hacks to improve the output include allowing the model to say 'I don't know', giving the model room to think by extracting quotes, breaking down complex tasks into subtasks, and checking the model's comprehension.

  • What are the iterating tips provided in the video for finding the best prompt?

    -Iterating tips for finding the best prompt include trying different prompts, combining examples with direct instructions, rephrasing instructions, trying different personas, and experimenting with the number of examples used in Few-Shot learning.

Outlines

00:00

📚 Introduction to Prompt Engineering

Patrick from Assembly AI introduces the video's focus on prompt engineering for large language models. He emphasizes that the video will not list the best prompts but will instead provide a guide to the basic concepts and fundamentals of prompt engineering. The goal is to improve viewers' understanding of how to effectively use prompts with language models. The video will cover the elements of a prompt, use cases, general tips, specific prompting techniques, and provide a list of resources for further learning.

05:02

🔍 Elements of a Prompt and Use Cases

The video delves into the five elements of a prompt: input or context, instructions, questions, examples, and desired output format. Patrick explains that while all elements are not mandatory, at least one instruction or question should be present for an effective prompt. He provides examples of each element and discusses various use cases for prompts, such as summarization, classification, translation, text generation, question answering, coaching, and image generation with certain models.

10:02

💡 Prompting Techniques and Tips

Patrick shares a list of guidelines to improve prompt effectiveness, including being clear and concise, providing relevant context, using examples, specifying output formats, and encouraging factual responses. He also introduces specific prompting techniques like length control, tone control, style control, audience control, context control, scenario-based guiding, and Chain of Thought prompting. Patrick demonstrates how these techniques can help control the output and achieve desired results from language models.

🛠️ Prompting Hacks and Iteration Tips

The video concludes with 'cool hacks' to enhance prompt outcomes, such as allowing the model to indicate 'I don't know' and giving the model 'room to think' by extracting quotes before answering. Patrick also suggests breaking down complex tasks and checking the model's comprehension. He offers iterating tips, encouraging viewers to experiment with different prompts, rephrasing instructions, trying various personas, and adjusting the number of examples provided. The video wraps up with a compilation of resources used and a mention of Assembly AI's lemur best practices guide for applying LLMs to audio.

Mindmap

Keywords

💡Prompt Engineering

Prompt Engineering refers to the skill of crafting input prompts that guide large language models to produce desired outputs. In the context of the video, it is the central theme around which the entire discussion revolves. Patrick emphasizes the importance of understanding prompt engineering to get the best results from language models, suggesting that it involves a mix of basic concepts and practical techniques.

💡Large Language Models

Large Language Models (LLMs) are advanced AI systems designed to understand and generate human-like text based on the input they receive. The video discusses how to work effectively with these models, indicating that prompt engineering is crucial for leveraging their capabilities.

💡Elements of a Prompt

The video outlines that a prompt can consist of five elements: input or context, instructions, questions, examples, and a desired output format. These elements are essential for structuring prompts that elicit accurate and relevant responses from language models.

💡Use Cases

Use cases in the video refer to the various applications of prompt engineering, such as summarization, classification, translation, text generation, question answering, coaching, and image generation. These examples illustrate the broad utility of prompt engineering across different tasks and domains.

💡General Tips

General tips provided in the video are guidelines to improve the effectiveness of prompts. These include being clear and concise, providing relevant context, giving examples, specifying output formats, and encouraging factual responses. These tips are meant to enhance the interaction with language models.

💡Specific Prompting Techniques

Specific prompting techniques are methods to control the output of language models, such as length control, tone control, style control, audience control, context control, and scenario-based guiding. The video uses these techniques as examples to demonstrate how to direct the model's output more precisely.

💡Chain of Thought Prompting

Chain of Thought Prompting is a technique highlighted in the video where the prompt includes a step-by-step reasoning process to guide the model to the correct answer. This is particularly useful for complex questions and helps the model understand the logical progression needed to solve a problem.

💡Avoiding Hallucination

Hallucination in the context of language models refers to the generation of incorrect or fabricated information. The video suggests techniques to avoid this, such as explicitly instructing the model not to make things up and to use reliable sources to back up claims.

💡Iterating

Iterating in prompt engineering involves trial and error to find the most effective prompts. The video suggests trying different prompts, rephrasing instructions, and experimenting with various personas and examples to refine the prompts and achieve better results.

💡Cool Hacks

Cool Hacks mentioned in the video are innovative techniques to improve prompt outcomes, such as allowing the model to indicate 'I don't know', giving the model 'room to think' by extracting quotes, breaking down complex tasks, and checking the model's comprehension. These hacks are presented as creative ways to enhance interaction with language models.

Highlights

Patrick from Assembly AI introduces the basics of prompt engineering for large language models.

The video provides a guide to prompt engineering with basic concepts and fundamentals.

A good prompt should include at least one instruction or question.

Prompts can have five elements: input, instructions, questions, examples, and desired output format.

Instructions should be clear, like 'translate from English to German'.

Questions can refer to the input, such as 'what is the meaning of life?'

Examples, or view shots, help the model understand the expected output format.

Desired output format can specify the model's response style, like 'yes or no' or 'short answer with explanation'.

Use cases for prompts include summarization, classification, translation, and text generation.

Prompting techniques include length control, tone control, style control, audience control, and context control.

Chain of Thought prompting helps with complex questions by providing a step-by-step thought process.

Avoiding hallucination can be achieved by instructing the model to use reliable sources or not to make things up.

Cool hacks include letting the model say 'I don't know' and giving it room to think before responding.

Iterating tips involve trying different prompts, rephrasing instructions, and testing different personas.

Assembly AI offers a lemur best practices guide for working with their limo framework.

The video concludes with a list of resources used for the guide on prompt engineering.