Character.AI has taken a significant leap in the field of Prompt Engineering, recognizing its critical role in their operations. The company’s approach to constructing prompts is remarkably comprehensive, taking into account a multitude of factors such as conversation modalities, ongoing experiments, Character profiles, chat types, user attributes, pinned memories, user personas, and entire conversation histories. This level of detail is necessitated by the sheer volume of prompts they generate daily—billions—and the need to maximize the potential of expanding LLM context windows. Faced with diverse use cases, Character.AI has advocated for a paradigm shift from traditional ‘prompt engineering’ to ‘prompt design’. This evolution aims to move beyond mundane string manipulations toward creating precise and engaging prompts. To achieve this goal, they have developed an innovative library called Prompt Poet.
Character.AI’s innovative approach to prompt design is embodied in their newly developed tool, Prompt Poet. While Python f-strings have become the industry standard for Prompt Engineers, ranging from simple query insertions to complex string manipulations, this method often requires coding skills, limiting accessibility for non-technical users. Prompt Poet addresses this challenge by providing a more intuitive and efficient solution for both developers and non-technical individuals to design and manage production prompts. This tool significantly reduces the time spent on engineering string manipulations, allowing users to focus on crafting optimal prompts. Drawing inspiration from UI design principles, Prompt Poet conceptualizes a prompt as a function of the runtime state, encompassing elements such as the prompt template, data, token limit, and other relevant factors. This approach marks a significant step towards making prompt design more accessible and efficient for a wider range of users
Prompt = F(state)
Prompt Poet revolutionizes the process of prompt creation by shifting the focus from engineering to design. This innovative tool utilizes a combination of YAML and Jinja2 for templates, offering both flexibility and ease of combination. The template processing in Prompt Poet occurs in two key stages: rendering and loading. During the rendering stage, Jinja2 processes the input data, executing control flow logic, validating and binding data to variables, and evaluating template functions. The loading stage transforms the rendered output into a structured YAML file, which is then converted into Python data structures. Each part of the prompt is characterized by specific attributes: a human-readable name, the actual content string, an optional role specifier for distinguishing between users or system components, and an optional truncation priority. This structured approach allows for more efficient prompt management and enables both technical and non-technical users to create and iterate on prompts with greater ease and precision.
The combination of Jinja2 and YAML in Prompt Poet creates a powerful and flexible templating system. Jinja2 brings dynamic capabilities to the templates, allowing for direct data bindings, arbitrary function calls, and basic control flow structures. This flexibility enables users to create complex, context-aware prompts that can adapt to various scenarios. Meanwhile, YAML provides a structured format for the templates, with a depth of one level, which is crucial for implementing sophisticated truncation strategies when token limits are reached. This structured approach ensures that prompts remain coherent and effective even when they need to be shortened.Â
Character.AI’s commitment to continuous improvement is evident in its approach to model alignment with user preferences. By utilizing Prompt Poet, they’ve created a system that allows for seamless reconstruction of production prompts in offline processes, such as evaluation and post-training workloads. This templatization of prompts brings significant advantages to their workflow. It enables easy sharing of template files across different teams within the organization, eliminating the need to piece together various parts of their constantly evolving codebase. This streamlined approach not only enhances collaboration but also ensures consistency in prompt design across different stages of development and deployment.Â
Jinja2’s ability to invoke arbitrary Python functions within templates at runtime is a key feature of Prompt Poet. This functionality enables on-the-fly data retrieval, manipulation, and validation, streamlining prompt construction. For instance, an `extract_user_query_topic` function could process a user’s query for use in template control flow, potentially involving a round-trip to a topic classifier. This feature significantly enhances the dynamic capabilities of prompt design.
Prompt Poet defaults to the TikToken “o200k_base†tokenizer but allows for alternate encoding names via the `tiktoken_encoding_name` parameter. Users can also provide their encoding function using the `encode_func` parameter, which should be a callable that takes a string and returns a list of integers. This flexibility allows for customization of the tokenization process to suit specific needs.
For LLM providers supporting GPU affinity and prefix cache, Character.AI’s truncation algorithm can maximize the prefix-cache rate. This rate is the proportion of prompt tokens retrieved from the cache to total prompt tokens. Users should find optimal values for the truncation step and token limit for their use case. Increasing the truncation step raises the prefix cache rate but results in more tokens being truncated from the prompt.
Character.AI’s truncation strategy achieves a remarkable 95% cache rate by optimizing message truncation. The strategy involves truncating to a fixed point, moving this point on average every k turns. This approach maximizes the use of GPU prefix cache, as described in “Optimizing Inferenceâ€. While this method often truncates more than is strictly necessary, it significantly outperforms simple token limit-based truncation in terms of cache utilization.
In a typical chat scenario with messages M1 to M10, naive truncation to just below the token limit causes the truncation point to shift every turn. This leaves only a small portion of the prefix retrievable from the cache, resulting in significant recomputation costs. This approach fails to take full advantage of the GPU prefix cache.
Character.AI’s cache-aware truncation algorithm maintains a fixed truncation point for every k turns. This approach preserves an unbroken sequence of tokens up to the most recent message, allowing reuse of computations from the previous turn stored in GPU prefix cache. The value of k is determined by the truncation step and the average number of tokens per truncated message.
Prompt Poet revolutionizes prompt engineering by shifting focus from manual string manipulations to intuitive design. It simplifies complex prompt creation, enhancing AI-user interactions. By empowering both technical and non-technical users to prioritize design over engineering, Prompt Poet has the potential to transform AI interactions, making them more efficient and user-centric. As large language models continue to evolve, tools like Prompt Poet will be crucial in maximizing their potential in user-friendly ways.
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