Prompt Engineering Techniques (extended version)

Jonathan Yarkoni
14 Dec 202352:38

TLDRJonathan Yonei, an AI consultant and founder of Shujin, leads a webinar on prompt engineering, a skill crucial for guiding AI models to provide desired responses. He shares a spectrum of techniques, from basic to advanced, and demonstrates their application through hands-on examples. Yonei covers the prompt engineering process, discusses influential papers like Chain of Thought, and offers resources for further learning. The session includes interactive demos, illustrating how to transform basic prompts into effective ones, and concludes with a look at the future of prompt engineering in AI.

Takeaways

  • 😀 Prompt Engineering is the skill of effectively crafting prompts to guide AI models to give desired responses.
  • 🔧 The process involves basic to advanced techniques, including setting the task, providing context, defining roles, formatting, and applying constraints.
  • 🎯 The webinar showcased several demos to illustrate the step-by-step improvement of prompts, emphasizing the iterative nature of prompt engineering.
  • 📚 Advanced techniques discussed include Chain of Thought, knowledge generation, emotional stimuli, and self-consistency, which can significantly enhance model performance.
  • 💡 The importance of being specific, removing fluff, explaining terms, and prioritizing tasks was highlighted to create effective prompts.
  • 📉 Recency bias and the 'lost in the middle' effect were discussed, indicating the model's tendency to focus more on recent or endpoint information in long prompts.
  • 🔗 The concept of using examples to improve prompt performance was explored, with the suggestion that more examples can lead to better model responses.
  • 🔄 The iterative process of prompt engineering involves stating the problem, proposing a solution, testing, examining output, and repeating until an effective prompt is crafted.
  • 📧 A demo on creating a cold email using prompt engineering showcased the application of these techniques, resulting in a refined and effective email template.
  • 🔮 Future predictions suggest that prompt engineering will evolve to include longer, more complex prompts akin to computer programs, potentially reducing the need for traditional software engineering in some areas.

Q & A

  • What is the background of Jonathan Yonei, the presenter of the webinar?

    -Jonathan Yonei comes from a background in software engineering with around 15 years of experience in the industry. He previously spent six years at Google as an AI and ML specialist and recently founded an AI consulting company called Shujin.

  • What is the main topic of the webinar that Jonathan Yonei is presenting?

    -The main topic of the webinar is 'Prompt Engineering Techniques', which involves the skill of effectively crafting prompts to guide AI models into giving the desired responses.

  • What are the key components of a prompt according to the webinar?

    -The key components of a prompt include the task, context, role, formatting, tone, and constraints. These elements guide the AI model to provide the correct or desired response.

  • How does the concept of 'Chain of Thought' improve the performance of AI models as discussed in the webinar?

    -The 'Chain of Thought' concept involves providing the AI model with step-by-step reasoning or calculations to solve a problem. By demonstrating the thought process, the model can emulate the steps and improve its accuracy, as shown in the webinar to increase benchmark performance by 34%.

  • What is the role of 'emotional stimuli' in prompt engineering as mentioned by Jonathan Yonei?

    -Emotional stimuli refers to appending phrases to the end of a prompt that appeal to the emotional side of the AI model. This technique has shown to improve performance, as if the model has a self-monitoring or social cognitive side that responds positively to such stimuli.

  • How does 'self-consistency' work in the context of prompt engineering?

    -Self-consistency involves querying the model multiple times with the same prompt and then selecting the most common or consistent answer. This method leverages the model's tendency to provide similar responses when faced with the same input repeatedly.

  • What is the purpose of 'recursive criticism and improvement' in prompt engineering?

    -Recursive criticism and improvement involve using the AI model to critique the prompt itself and then incorporating that critique into the prompt. This method helps refine the prompt iteratively to achieve better results.

  • Can you explain the 'reverse prompt engineering' technique mentioned in the webinar?

    -Reverse prompt engineering starts with a desired output and asks the AI model to generate the prompt that would produce such an output. This technique is useful for creating templates or generating multiple similar outputs based on a successful example.

  • What are some advanced techniques for prompt engineering discussed in the webinar?

    -Advanced techniques discussed include Chain of Thought, knowledge generation, step back and abstraction, emotional stimuli, self-consistency, recursive criticism and improvement, and reverse prompt engineering.

  • How does Jonathan Yonei suggest improving a prompt in the iterative process of prompt engineering?

    -Jonathan Yonei suggests an iterative process of stating the problem, proposing an initial solution, testing, examining the output, doing research, and adjusting the solution. This process may involve multiple iterations and refinements until the prompt produces satisfactory results.

  • What resources does Jonathan Yonei recommend for learning more about prompt engineering?

    -Jonathan Yonei recommends resources such as 'Learn Prompting' on Dorg, 'Master the Perfect Prompt' YouTube video, and 'Write Expert Prompt' on Medium. He also mentions that they conduct workshops for a deeper understanding of prompt engineering.

Outlines

00:00

👨‍💻 Introduction to Prompt Engineering

Jonathan Yonei, a software engineer with 15 years of experience and a background in AI at Google, introduces the webinar on prompt engineering. He discusses his recent venture, an AI consulting company called Shujin, and outlines the webinar's agenda, which includes demos, basic to advanced techniques, and resources for learning more about prompt engineering. The concept of prompt engineering is explained as the skill of crafting effective prompts for AI models, guiding them to provide desired responses. The session aims to teach attendees how to refine prompts to achieve better AI output.

05:01

📚 Advanced Prompt Engineering Techniques

The paragraph delves into advanced prompt engineering techniques, highlighting papers such as Chain of Thought, knowledge generation, and step back prompting. These methods aim to enhance the model's performance by guiding it through logical steps, providing background knowledge, and abstracting complex queries. The effectiveness of these techniques is discussed, with examples showing significant improvements in model responses when applied correctly.

10:03

🧠 Emotional Stimuli and Self-consistency in Prompts

This section explores the role of emotional stimuli in prompts, suggesting that appeals to a model's 'emotional side' can improve performance. It also covers self-consistency, where the model's answers are more reliable when queried multiple times and the most frequent response is chosen. The paragraph emphasizes the intuitive nature of these techniques and their ease of implementation in prompt engineering.

15:04

🔄 Iterative Prompt Engineering Process

The paragraph explains the iterative nature of prompt engineering, starting with a basic prompt and refining it through testing, examination, and research. It emphasizes the importance of domain knowledge and the step-by-step process of improving prompts. The speaker shares his experience, noting that even after nearly a year of working with generative AI, he has never written a perfect prompt on the first try.

20:08

📝 Crafting a Cold Email Prompt

The speaker demonstrates the process of creating a prompt for generating a cold email, starting with a basic assignment of role and task. Through iterations, the prompt is refined by adding engagement rules, context, and specificity. The final prompt includes steps for the model to follow, such as checking the speaker's website and incorporating an item photo into the email. The result is a concise and appealing email tailored to the speaker's online sports retailer business.

25:08

🔮 Future of Prompt Engineering

The final paragraph speculates on the future of prompt engineering, predicting longer, more complex prompts akin to computer programs. It suggests that prompts will become more controllable and that models will allow for finer manipulation through prompt engineering techniques. The speaker also envisions a future where non-technical roles will be able to accomplish more through prompt engineering, potentially reducing the need for traditional R&D personnel.

30:08

📈 Conclusion and Community Engagement

The webinar concludes with a call to action for community engagement, encouraging participants to vote on the next topic and join future meetups. The speaker, Jonathan, thanks attendees for joining, provides his contact information for follow-ups, and emphasizes the value of the community in learning and advancing prompt engineering practices.

Mindmap

Keywords

💡Prompt Engineering

Prompt engineering refers to the skill of effectively crafting prompts to guide AI models, like chatbots, to provide desired responses. In the context of the video, it is central to the discussion as the speaker, Jonathan, shares various techniques to improve the interaction with AI models. The video emphasizes the iterative process of refining prompts to achieve more accurate and relevant outputs from AI systems.

💡AI and ML Specialist

An AI and ML Specialist is a professional with expertise in artificial intelligence and machine learning. Jonathan, the speaker, has a background in software engineering and has spent six years at Google as an AI and ML Specialist. This experience positions him as an authority on the topic of prompt engineering, as he has hands-on experience with the practical applications of AI and ML in real-world scenarios.

💡Context

In the video, 'context' is highlighted as a crucial part of crafting effective prompts. It involves providing additional information to the AI model to guide its response. The more context given, the better the model can perform, as it helps 'spark the memory' of the model. For instance, when Jonathan discusses the guard allocation problem, he provides detailed context about the guards, their shifts, and the constraints to help the AI generate an accurate schedule.

💡Role

The 'role' in prompt engineering is about instructing the AI model to act as a specific character or professional. This can range from a mathematician to a philosopher. In the video, Jonathan uses the role of a 'mathematician' to trigger an analytical approach in the AI's output for solving the guard allocation problem. This technique helps in eliciting a particular style or perspective in the AI's response.

💡Chain of Thought

Chain of Thought is a technique mentioned in the video where the AI model is provided with step-by-step reasoning to solve a problem. This method helps the model emulate the thought process, leading to improved responses. Jonathan demonstrates this by giving the AI a math problem and then providing the steps to solve it, which enables the model to understand and replicate the process for similar problems.

💡Recency Bias

Recency Bias refers to the tendency of AI models to give more weight to information that appears towards the end of a prompt. Jonathan discusses this concept, noting that when crafting prompts, it's important to be aware of this bias to ensure that critical information is not overlooked by the model. This understanding helps in structuring prompts effectively to achieve desired outcomes.

💡Lost in the Middle

This concept, mentioned in the video, highlights that information in the middle of a long prompt might be less impactful on the AI model's output compared to information at the beginning or end. It underscores the importance of prompt structure in ensuring that all relevant details are effectively communicated to the model.

💡Shujin

Shujin is the AI consulting company founded by Jonathan. The company takes on projects across various AI domains, including classical machine learning, generative AI, and MLOps. The mention of Shujin in the video establishes Jonathan's current work and expertise in the field of AI, adding credibility to his discussion on prompt engineering.

💡Generative AI

Generative AI is a subset of AI that focuses on creating new content, such as text, images, or music, based on existing data. In the video, Jonathan discusses projects around prompt engineering within the spectrum of generative AI, indicating the application of prompt engineering in generating new and original content through AI.

💡Mlops

MLOps stands for Machine Learning Operations and refers to the practices for collaboratively managing the lifecycle of machine learning projects. Jonathan mentions that Shujin works on projects across the spectrum, including Mlops, which involves the deployment, monitoring, and maintenance of machine learning models in production environments.

Highlights

Introduction to prompt engineering as a skill to effectively craft prompts for AI models.

The importance of context in guiding AI models to produce desired responses.

Using role assignment to influence the model's approach to generating output.

Formatting prompts to match desired output styles, such as bullet points or essay style.

Incorporating tone into prompts to reflect formality or mimic famous personalities.

Setting constraints to guide the model within specific boundaries.

Chain of Thought prompting technique to guide the model through logical steps.

Knowledge generation as a method to enhance the model's performance.

Emotional stimuli's impact on improving model performance.

Self-consistency technique to improve model reliability.

Recursive criticism and improvement for refining prompts.

Reverse prompt engineering to deduce prompts from successful outputs.

The iterative process of prompt engineering from problem statement to solution.

Guard placement problem as an example of complex prompt engineering.

Writing effective cold emails through refined prompts and role assignment.

Importance of specificity and clarity in prompt engineering for better model output.

Future predictions for prompt engineering including longer prompts and increased control.

Resources recommended for learning more about prompt engineering.