Prompt trees are structured frameworks used to organise and guide interactions with AI language models. They break down complex tasks into a sequence of smaller, manageable prompts, often branching based on user input or AI responses. This method helps ensure that conversations or processes with AI follow a logical path and cover all necessary steps.
Modular prompts are a way of breaking down complex instructions for AI language models into smaller, reusable parts. Each module focuses on a specific task or instruction, which can be combined as needed to create different prompts. This makes it easier to manage, update, and customise prompts for various tasks without starting from scratch every…
Task pooling is a method used to manage and distribute work across multiple workers or processes. Instead of assigning tasks directly to specific workers, all tasks are placed in a shared pool. Workers then pick up tasks from this pool when they are ready, which helps balance the workload and improves efficiency. This approach is…
Input hooks are special pieces of code that allow a program to watch for and react to specific user inputs, such as keyboard presses or mouse movements. They act like listeners, waiting for certain actions so that the software can respond immediately. This mechanism is often used to customise or extend how a program handles…
Workflow loops are repeating steps within a process that continue until certain conditions are met. These loops help automate tasks that need to be done multiple times, such as checking for new emails or processing a list of items. By using workflow loops, teams can save time and reduce errors in repetitive work.
Hyperparameter tweaks refer to the process of adjusting the settings that control how a machine learning model learns from data. These settings, called hyperparameters, are not learned by the model itself but are set by the person training the model. Changing these values can significantly affect how well the model performs on a given task.
Fine-tune sets are collections of data specifically chosen to train or adjust an existing artificial intelligence model, making it perform better on a certain task or with a particular type of input. These sets usually contain examples and correct answers, helping the AI learn more relevant patterns and responses. Fine-tuning allows a general model to…
Curriculum setup refers to the process of organising and structuring the content, lessons, and activities that make up a course or educational programme. It involves selecting topics, arranging them in a logical order, and deciding how each part will be taught and assessed. A well-planned curriculum setup ensures that learners progress through material in a…
Relevance rate measures how well a piece of content, product, or recommendation matches what a user is looking for or needs. It is often calculated as the percentage of items shown that are considered relevant by users or meet specific criteria. A high relevance rate indicates that the system is successfully providing information or options…
Drift scores are numerical values that measure how much data has changed over time compared to a previous dataset. They help identify shifts or changes in the patterns, distributions, or characteristics of data. These scores are often used to monitor whether data used by a machine learning model is still similar to the data it…