TLDR;
This roundtable discussion explores the multifaceted nature of prompt engineering, defining it as a blend of clear communication, iterative experimentation, and system integration to elicit desired behaviors from language models. The panel discusses the qualities of a good prompt engineer, emphasizing the importance of clear communication, iteration, and the ability to anticipate potential failure points. They also touch on the use of honesty, personas, and metaphors in prompts, the role of model reasoning, and the differences between enterprise, research, and general chat prompts. The discussion concludes with tips for improving prompting skills, a brief exploration of jailbreaking, and insights into the evolution and future of prompt engineering.
- Prompt engineering is a blend of clear communication, iterative experimentation, and system integration.
- A good prompt engineer should have clear communication skills, be able to iterate, and anticipate potential failure points.
- The future of prompt engineering involves models that can elicit information from users and adapt to different contexts.
Introduction [0:00]
The roundtable session focuses on prompt engineering, gathering diverse perspectives from research, consumer, and enterprise angles to explore its essence and implications. The discussion aims to uncover the various facets of prompt engineering and its significance in different contexts.
Defining prompt engineering [2:05]
Prompt engineering is defined as the process of eliciting specific behaviors from a model through clear communication and iterative experimentation. It involves working with the model to achieve tasks that would otherwise be impossible, akin to communicating with a person but with the added benefit of a "restart button" for refining prompts. Integrating prompts within a system is crucial, as it often requires more than just a single prompt to achieve the desired outcome. Prompts can be viewed as a form of programming, necessitating consideration of data sources, latency trade-offs, and overall system design.
What makes a good prompt engineer [6:34]
A good prompt engineer possesses clear communication skills, the ability to iterate, and the capacity to anticipate potential failure points in prompts. Iteration involves sending numerous prompts to the model, analyzing misinterpretations, and refining the prompt accordingly. It's crucial to consider unusual cases and potential ambiguities in prompts to ensure they function correctly across various scenarios. Reading model outputs closely is essential to verify that the model is interpreting instructions as intended. A good prompt engineer can step back from their own knowledge and communicate the necessary information to the model effectively.
Refining prompts [12:17]
Models may not always ask clarifying questions like humans, so prompt engineers must anticipate potential misunderstandings and address them proactively. One approach is to ask the model to identify ambiguities or unclear instructions in the prompt. Additionally, if the model makes a mistake, it can be asked to explain why and suggest improvements to the instructions. Experimentation and iteration are key to refining prompts and understanding the model's behavior. It's important to "hammer on" the model to test its reliability and identify edge cases where it may be less reliable. A well-constructed set of prompts can provide more valuable insights than a larger, less carefully crafted set.
Honesty, personas and metaphors in prompts [24:27]
As models become more capable, lying to them or using contrived personas may not be necessary. Instead, it's often more effective to clearly communicate the actual task and context to the model. However, metaphors can be helpful in certain cases to guide the model's thinking. The key is to be prescriptive about the exact situation and context in which the model is being used. Role prompting can be a useful technique, but it should not be used as a shortcut to avoid providing the model with the necessary details about the task.
Model reasoning [37:12]
Chain of thought prompting, which involves having the model explain its reasoning before providing an answer, can improve outcomes. Structuring the reasoning and iterating with the model on how it should reason can further enhance performance. While the exact nature of model reasoning is debated, it's clear that it contributes to the outcome. Good grammar and punctuation are not necessarily required in prompts, but attention to detail is important. The level of styling in prompts is a matter of personal preference.
Enterprise vs research vs general chat prompts [45:18]
Research prompts often prioritize variety and diversity, while enterprise prompts emphasize reliability and consistency. In research, examples may be used to illustrate the task without being too similar to the data the model will see. Enterprise prompts require thorough testing against a wide range of inputs and potential use cases. Chat prompts allow for human-in-the-loop interaction and iterative refinement, while prompts for chatbot systems must cover the entire spectrum of possible encounters.
Tips to improve prompting skills [50:52]
To improve prompting skills, it's recommended to read prompts, read model outputs, experiment, and talk to the model frequently. Giving prompts to another person for feedback can also be helpful. It's important to enjoy the process and be curious about the model's behavior. Trying to get the model to do something you don't think it can do can be a valuable learning experience.
Jailbreaking [53:56]
Jailbreaking involves finding the boundary limits of what a model can do and figuring out how it responds to different phrasings and wordings. It may involve putting the model out of distribution from its training data or exploiting vulnerabilities in the training process. Jailbreaking can be a mix of hacking, social engineering, and understanding the system and its training.
Evolution of prompt engineering [56:51]
Prompt engineering has evolved as models have become more capable. Techniques that were once effective may no longer be necessary as models are trained to incorporate them. It's important to respect the model and provide it with as much information and context as possible. Giving the model papers to read can be an effective way to teach it new techniques. Prompting now involves imagining oneself in the place of the model and adapting one's approach accordingly.
Future of prompt engineering [1:04:34]
The future of prompt engineering may involve models that can help users with prompting and elicit information from them. As models become more capable, they may be able to set goals and ask clarifying questions. Prompt engineering may evolve from providing instructions to consulting with an expert. The skill of introspection, or making oneself legible to the model, may become more important. Philosophy, with its emphasis on clear communication and defining concepts, may become more relevant to prompting.