Prompt Design at Character.AI

🌈 Abstract

The article discusses Prompt Poet, a tool developed by Character.AI to streamline the process of prompt engineering for large language models. It highlights the shift from traditional "prompt engineering" to "prompt design", which aims to make prompt creation more accessible and efficient for both developers and non-technical users.

🙋 Q&A

[01] Prompt Design at Character.AI

1. What are the key challenges faced by Character.AI in prompt engineering?

• The need to consider a wide array of data and factors, such as current conversation modalities, ongoing experiments, the Characters involved, chat types, various user attributes, pinned memories, user personas, and the entire conversation history.
• The billions of prompts constructed per day, the need to maximize the use of expanding LLM context windows, and the diversity of use cases.

2. How does Prompt Poet address these challenges?

• Prompt Poet allows both developers and non-technical users to efficiently design and manage their production prompts.
• It saves time on engineering string manipulations, enabling everyone to focus more on crafting the optimal prompts for their users.
• Prompt Poet considers a prompt as a function of runtime state, including the prompt template, data, token limit, and more.

[02] Prompt Templates

1. How do Prompt Poet's prompt templates work?

• Prompt templates use a mix of YAML and Jinja2, making them flexible and easy to combine.
• The template processing involves two stages: rendering (Jinja2 processing) and loading (structured YAML file).
• The YAML structure consists of repeated blocks or parts, each with attributes like name, content, role, and truncation priority.

2. What are the key design choices behind Prompt Poet's templating language?

• The combination of Jinja2 and YAML provides an extensible and expressive templating language.
• Jinja2 facilitates direct data bindings, arbitrary function calls, and basic control flow within templates.
• YAML provides structure to the templates, allowing for sophisticated truncation when the token limit is reached.

[03] Prompt Poet Library

1. What are the key features of the Prompt Poet Library?

• Tokenization and truncation features to support efficient caching and low latency responses.
• Prompt properties like tokenize(), truncate(), string, tokens, messages, and parts.

2. How does Prompt Poet's truncation strategy work?

• Prompt Poet's cache-aware truncation algorithm truncates up to a fixed truncation point every k turns, allowing for maximum exploitation of GPU prefix cache.
• This approach is more efficient than naive truncation, which would shift the truncation point every turn, resulting in significant recomputation costs.

[04] Conclusion

1. What are the key benefits of Prompt Poet?

• Shift the focus from cumbersome manual string manipulations to a more streamlined and intuitive design-focused approach.
• Empower both developers and non-technical users to focus more on prompt design and less on prompt engineering.
• Reshape how we interact with AI, making these interactions more efficient, intuitive, and aligned with user needs.

2. How does Prompt Poet compare to related work in the field?

• Prompt Poet shares similarities with projects like Priompt, dspy, Prompt Engine, llm, and various Python f-string wrappers, but it offers a more comprehensive and structured approach to prompt templating and design.