Open Source Generative Process Automation (i.e. Generative RPA). AI-First Process Automation with Large ([Language (LLMs) / Action (LAMs) / Multimodal (LMMs)] / Visual Language (VLMs)) Models
Automated Prompt Engineering: The use of algorithms or models to generate or optimize prompts for interacting with language or multimodal models automatically.
Meta-Learning or Learning to Learn: While not exclusively about prompt generation, this term is relevant for systems that improve their ability to generate prompts over time, based on feedback or performance metrics.
Self-Improving Models: Models that refine their prompt generation capabilities autonomously, improving their interactions with other models or data sources.
Chain-of-Thought Prompting: A strategy where prompts are designed to elicit step-by-step reasoning from the model, which can be automated for generating complex task prompts.
Zero-Shot and Few-Shot Learning: Techniques that involve generating prompts that enable a model to perform tasks without or with minimal task-specific training data, emphasizing the model's ability to generalize from limited examples.
Task Formulation as Prompt Generation: A perspective on transforming traditional machine learning tasks into prompt-based tasks that can be addressed by large models, focusing on the automated creation of such prompts.
Programmatic Prompt Generation: The use of software or scripts to dynamically create prompts based on specific criteria or data inputs, often used in conjunction with large models for scalable applications.
Feature request
This task involves exploring the concepts related to the exchange here: https://chatgpt.com/share/d9cc7ec6-0d34-4023-92c9-c577311012b0
Automated Prompt Engineering: The use of algorithms or models to generate or optimize prompts for interacting with language or multimodal models automatically.
Meta-Learning or Learning to Learn: While not exclusively about prompt generation, this term is relevant for systems that improve their ability to generate prompts over time, based on feedback or performance metrics.
Self-Improving Models: Models that refine their prompt generation capabilities autonomously, improving their interactions with other models or data sources.
Chain-of-Thought Prompting: A strategy where prompts are designed to elicit step-by-step reasoning from the model, which can be automated for generating complex task prompts.
Zero-Shot and Few-Shot Learning: Techniques that involve generating prompts that enable a model to perform tasks without or with minimal task-specific training data, emphasizing the model's ability to generalize from limited examples.
Task Formulation as Prompt Generation: A perspective on transforming traditional machine learning tasks into prompt-based tasks that can be addressed by large models, focusing on the automated creation of such prompts.
Programmatic Prompt Generation: The use of software or scripts to dynamically create prompts based on specific criteria or data inputs, often used in conjunction with large models for scalable applications.
Related:
Motivation
Implement "prompt automation" in the system diagram.