spdustin/ChatGPT-AutoExpert
ChatGPT AutoExpert
ChatGPT-AutoExpert is an AI prompt engineering framework and persona management system designed to improve the technical accuracy and nuance of large language model outputs. It provides a collection of curated system prompts and custom instructions to refine user queries and remove conversational filler.
The system employs a persona framework to assign specialized expert roles based on the request context. It utilizes a command shortcut system that maps short text sequences to complex instructional sets, enabling the rapid execution of repetitive tasks.
For software development, the project includes a coding workflow optimizer that tracks project structures via file and symbol trees and manages sandbox environments for running code. It also incorporates chain-of-thought reasoning to make the logical steps of an answer transparent and supports session state persistence to maintain project context across different conversations.
Features
- Persona Management Systems - Ships a framework for creating, organizing, and assigning specialized expert personas to language models.
- System Prompts - Provides a curated collection of system prompts and instructions to improve technical accuracy and nuance.
- Persona Assignments - Assigns specialized expert roles based on request context to ensure domain-specific professional standards.
- Contextual Persona Frameworks - Implements a method for assigning specialized expert roles based on the specific context of user requests.
- System-Level Instructions - Provides a library of persistent system-level instructions to guide LLM behavior and technical nuance.
- Persona Injection Files - Injects domain-specific expert roles into system prompts to guide technical accuracy and tone.
- Prompt Engineering Libraries - Provides a collection of optimized instructions and shortcut commands as a reusable library for better AI interactions.
- Custom Instruction Sets - Offers a set of curated system prompts to improve technical accuracy and remove conversational filler from outputs.
- Interactive AI Conversations - Optimizes interaction speed and reduces AI filler through shortcut commands and refined response patterns.
- Persona Definitions - Assigns specialized expert roles via named configurations and system prompts to ensure domain-specific professional standards.
- Prompt Optimizers - Provides curated system instructions and refined query structures to increase the precision and quality of AI responses.
- Prompt Engineering Frameworks - Provides a structured system combining chain-of-thought reasoning, iterative refinement, and persona injection for high-quality outputs.
- Reasoning Models - Implements chain-of-thought reasoning to force the model to expose logical steps for transparent and accurate outputs.
- Chain-of-Thought Enforcement - Provides mechanisms to force the model to output step-by-step logical derivations for increased transparency.
- Reasoning Process Controllers - Configures the model to output its structured thinking process and logical steps alongside final answers.
- Response Depth Enhancements - Increases response depth and nuance while removing repetitive disclaimers and unnecessary conversational filler.
- Prompt Iteration - Rewrites vague user inputs into structured prompts to improve the precision and quality of model outputs.
- Chat Command Shortcuts - Triggers specific actions like summarizing or reviewing using short text commands for faster interaction.
- Chat Command Shortcuts - Implements a shortcut system that maps short text sequences to complex instructions for rapid task execution.
- User Query Rewriting - Transforms ambiguous user questions into precise, structured prompts to elicit higher-quality responses.
- AI Personas - Manages sets of system prompts that define behavioral identities and professional domain expertise.
- Persistent Context Management - Uses exported history and file trees to maintain a consistent project mental model across sessions.
- Autonomous Coding Workflows - Optimizes the coding process by tracking project structures and managing sandbox environments for multi-turn programming tasks.
- Project Structure Mapping - Tracks the hierarchy of files and symbols to maintain project context during multi-turn coding.
- Code Sandboxing Environments - Manages an isolated execution environment for running and editing generated code samples.
- Named Session Save and Restore - Exports and imports conversation history and files to resume work seamlessly across different sessions.
- Hierarchical File Mapping - Maintains a hierarchical representation of files and symbols to track progress in complex multi-turn coding tasks.
- Sandboxed Execution Environments - Runs generated code within an isolated sandbox environment using a custom kernel to manage libraries.
