AI prompt engineering: A deep dive

Key Concepts

• Prompt Engineering: The process of designing and refining text-based instructions (prompts) to elicit desired outputs from language models.
• Iteration: The cyclical process of creating a prompt, evaluating the model's response, and refining the prompt based on the evaluation.
• Clear Communication: The ability to articulate tasks and concepts in a way that is easily understood by the model.
• Edge Cases: Unusual or atypical scenarios that can cause a prompt to fail.
• Theory of Mind: The ability to understand how a model will interpret instructions.
• Chain of Thought: A prompting technique that encourages the model to explicitly explain its reasoning process before providing an answer.
• Pretrained Models: Language models trained on vast amounts of text data, primarily focused on text completion.
• RLHF (Reinforcement Learning from Human Feedback) Models: Language models that have been fine-tuned using human feedback to align their behavior with human preferences.
• Meta Prompts: Prompts designed to instruct a model on how to generate other prompts.
• Elicitation: The process of drawing out information from a user to create a more effective prompt.

What is Prompt Engineering?

• Prompt engineering is about maximizing a language model's potential by communicating effectively. It involves trial and error, similar to traditional engineering, but with the unique ability to "restart" and experiment from scratch.
• Zack Witten: "Prompt engineering is trying to get the model to do things, trying to bring the most out of the model... a lot of it is just clear communicating."
• The "engineering" aspect comes from the iterative process of experimentation and design.
• Prompts are integrated into larger systems, requiring consideration of data sources, latency, and system-level trade-offs.
• Prompts can be viewed as a form of natural language code, requiring precision, version control, and experiment tracking.
• David Hershey: "I think of prompts as the way that you program models a little bit... you have to think about where data comes from, what data you have access to... You have to think about trade-offs in latency and how much data you're providing."
• Writing a clear description of a task is often more effective than complex abstractions.

What Makes a Good Prompt Engineer?

• Clear communication skills are essential, but strong writing skills are not necessarily a primary indicator.
• The ability to iterate rapidly and analyze model responses is crucial.
• A good prompt engineer anticipates potential failure points and edge cases.
• Amanda Askell: "It's this willingness to iterate and to look and think what is it that was misinterpreted here, if anything? And then fix that thing."
• Reading model outputs closely is the prompting equivalent of "looking at your data" in machine learning.
• It's important to strip away assumptions and communicate the full set of information needed for a task.
• Good prompt engineers can step back from their own knowledge and communicate effectively with the model.
• Models currently struggle to ask clarifying questions, so prompt engineers must anticipate potential ambiguities.
• Models can sometimes identify their own mistakes and suggest improvements to the prompt.

Trusting the Model

• Trust in a model should not be assumed by default.
• Consistency in model outputs across a well-constructed set of prompts can increase confidence.
• Prompting can significantly impact experiment success, potentially making the difference between a failed and successful outcome.
• There is a risk of getting stuck in the pursuit of a "mythical, better prompt."
• Heuristics for determining if a task is possible include checking if the model "gets it" and evaluating its thought process.
• Amanda Askell: "I don't trust the model ever and then I just hammer on it... I can trust the model if I look at 100 outputs of it and it's really consistent."

Role-Playing and Honesty in Prompting

• The effectiveness of role-playing (e.g., telling the model it is a specific persona) is debated.
• As models become more capable, honesty and directness in prompts may be more effective than metaphors or lies.
• Amanda Askell: "I just don't see it as necessary to lie to them... If they understand the thing, just ask them to do the thing that you want."
• Metaphors can be helpful if they provide a useful framework for the model to think about the task.
• It's important to be prescriptive about the context in which the model is being used.
• The "temp agency" thought experiment can be useful: imagine you are explaining the task to a competent person with limited context.

Prompting Styles and Techniques

• Good grammar and punctuation are not strictly necessary, but attention to detail is important.
• The models have been trained to guess what you want them to act like.
• Enterprise prompts prioritize reliability and consistency, while research prompts emphasize variety and exploration.
• Illustrative examples can promote flexibility, while concrete examples can improve reliability.
• Giving the model "outs" (e.g., a way to indicate uncertainty) can improve data quality.
• Taking the evaluation yourself can provide valuable insights.

Chain of Thought Reasoning

• The extent to which chain-of-thought reasoning reflects genuine reasoning is debated.
• Structuring the reasoning process and providing examples can improve model performance.
• Even if the reasoning is not perfect, it appears to contribute to the outcome.
• David Hershey: "Whether or not that's reasoning or how you wanted to classify it, you can think of all sorts of proxies for how I would also do really bad if I had to do one-shot math without writing anything down. Maybe that's useful, but all I really know is, it very obviously does help."

Prompting Misconceptions

• People often treat prompts like Google search queries, using keywords instead of clear instructions.
• There is a tendency to obsess over individual lines of instruction instead of providing a holistic context.

Distinctions Between Prompt Types

• Research Prompts: Focus on exploring the model's capabilities and understanding its behavior.
• Enterprise Prompts: Aim for reliability, consistency, and robustness in real-world applications.
• Chat Prompts: Often involve iterative refinement and human-in-the-loop interaction.

One Tip for Improving Prompting Skills

• Zack Witten: "Reading prompts, reading model outputs... Try to break down what it's doing and why and maybe test it out myself."
• Amanda Askell: "Do it over and over again, give your prompts to other people. Try to read your prompts as if you are a human encountering it for the first time."
• David Hershey: "Trying to get the model to do something you don't think it can do... pressing the boundaries of what I think a model's capable of."

Jailbreaks

• Jailbreaks may exploit the model's out-of-distribution behavior or vulnerabilities in its training.
• They can involve a mix of hacking, social engineering, and understanding the model's training data.

How Prompt Engineering Has Changed

• Effective prompting "hacks" tend to be short-lived as they are incorporated into model training.
• Models are becoming more capable of handling complex instructions and context.
• It's increasingly important to "respect the model" and provide it with relevant information, such as research papers.
• Amanda Askell: "Give it the paper... I give it the paper and then I'm like, 'Here's a paper about prompting technique. I just want you to write down 17 examples of this.' And then it just does it 'cause I'm like, 'It read the paper.'"
• Prompting involves imagining yourself in the place of the model, and the capabilities you attribute to the model change over time.

The Future of Prompt Engineering

• The ability to clearly specify goals will remain important.
• Models will increasingly assist in the prompting process.
• Meta-prompting (using models to generate prompts) will become more common.
• Models may become so advanced that they prompt the user to elicit the necessary information.
• The role of the prompt engineer may shift from "temp agency employee" to "designer," consulting with the model to achieve the desired outcome.
• Elicitation of information from the user will become a critical skill.
• The focus may shift from teaching the model to making yourself legible to the model.
• Amanda Askell: "In the future, it might just be that they can elicit that from us, rather than us having to do it for them."
• Amanda Askell: "Externalize your brain."

Conclusion

Prompt engineering is a rapidly evolving field that requires a combination of technical skill, creative thinking, and effective communication. As language models become more sophisticated, the role of the prompt engineer is likely to shift from providing explicit instructions to eliciting information from users and collaborating with models to achieve complex goals. The ability to "externalize your brain" and make your intentions clear to the model will be a key skill for future prompt engineers.