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Agentic Training Schema
Raw
Readme.md

Strawberry Phi by rUv: Reflection-Utilized Validation

  • Reflection: Emphasizes the model’s core capability of self-reflection and self-correction.
  • Utilized: Highlights the active use of reflection in improving the model's reasoning process.
  • Validation: Signifies the model’s ability to validate its reasoning, detect errors, and refine outputs for accuracy.

Use Case Overview:

Strawberry Phi is an advanced multi-modal, agentic AI assistant designed for complex task handling across various domains. Developed by rUv, it uses reflection-tuning techniques to self-evaluate and correct reasoning errors. The model leverages advanced methodologies such as sequential, concurrent, recurrent, and reinforcement learning approaches for task management, planning, and execution. By incorporating multi-modal inputs and outputs (e.g., text, images, audio), it can manage various task complexities, adapt dynamically to user requirements, and continuously improve its performance. It ensures reliability by integrating self-reflection mechanisms and using Glaive's synthetic data generation for rapid fine-tuning and error minimization.

The reflection approach to training language models, as exemplified by Reflection 70B, is an innovative technique designed to improve model performance and reduce errors. Here's an explanation of how it works:

  1. Base Model: The process starts with a pre-existing large language model, in this case, Meta's Llama 3.1-70B Instruct model.

  2. Reflection-Tuning: This is the core technique that teaches the model to detect and correct mistakes in its own reasoning. It involves:

    a) Special Tokens: The model is trained to use special tokens like , , , , , and . These tokens structure the model's thought process.

    b) Reasoning Process: When given a query, the model first reasons through it within the tags. This allows the model to "think out loud" about the problem.

    c) Self-Correction: If the model detects an error in its reasoning, it uses tags to acknowledge the mistake and attempt to correct it. This process can occur multiple times within a single response.

    d) Final Output: Once satisfied with its reasoning, the model provides its final answer within tags.

  3. Synthetic Data Generation: Companies like Glaive create large datasets of synthetic data that include these reflection and correction processes. This data is used to fine-tune the base model.

  4. Training Process: The model is then trained on this synthetic data, learning to mimic the reflection and self-correction processes embedded in the training examples.

  5. Iterative Improvement: Through multiple rounds of training, the model learns to apply this reflection process to a wide variety of queries and scenarios.

  6. Evaluation and Refinement: The model is tested on various benchmarks, and its performance is used to further refine the training process and data generation.

The key innovation of this approach is that it teaches the model not just to provide answers, but to critically evaluate its own reasoning and correct itself when necessary. This leads to more accurate and reliable outputs, especially in complex reasoning tasks.

This reflection-tuning technique represents a significant advancement in language model training, potentially reducing hallucinations and improving the overall reliability of AI-generated responses.

Overview

This configuration file defines a sophisticated multi-modal agentic AI assistant capable of handling complex tasks across various domains. It leverages Glaive's schema-based approach and synthetic data generation capabilities to create a highly customized and efficient model.

Key Components

  1. Model Basics

    • Name: AdvancedMultiModalAgenticAssistant
    • Base Model: phi-mini-128k
    • System Prompt: Defines the AI's core capabilities and goals

  2. Agentic Approaches

    • Includes various AI methodologies like sequential, concurrent, recurrent, and reinforcement learning approaches
    • Incorporates advanced techniques such as Q* and other hybrid approaches

  3. Dataset Schema

    • Defines structured input and output formats
    • Includes comprehensive fields for task understanding, planning, execution, and self-reflection

  4. Training Parameters

    • Specifies epochs, batch size, learning rate, and other hyperparameters

  5. Evaluation Metrics

    • Lists various metrics to assess model performance

  6. Fine-tuning Strategy

    • Outlines initial and specialized training phases
    • Includes provisions for continual learning

  7. Advanced Features

    • Error handling mechanisms
    • Bias mitigation strategies
    • Personalization capabilities
    • Performance tracking
    • Multi-task learning support
    • Human-in-the-loop integration

Usage

  1. Customize the JSON file according to your specific use case and requirements.
  2. Use Glaive's platform to generate synthetic data based on this schema.
  3. Train your model using Glaive's custom model training capabilities.
  4. Utilize Glaive's API for model deployment and integration into your applications.

Best Practices

  • Regularly update and refine your model based on performance metrics and user feedback.
  • Leverage Glaive's rapid iteration capabilities for continuous improvement.
  • Ensure compliance with ethical AI guidelines and data privacy regulations.

Support

For more information on using this configuration with Glaive.ai, please refer to the official Glaive documentation or contact their support team.

Capabilities

  1. Model:

    • Name: AdvancedMultiModalAgenticAssistant
    • Base Model: phi-mini-128k

  2. Multi-modal processing:

    • Ability to handle various input types (text, images, audio, etc.)
    • Integration of different data modalities for comprehensive understanding

  3. Agentic approaches:

    • Sequential (Chain of Thought, Tree of Thoughts, Plan-and-Solve)
    • Concurrent (Multi-agent collaboration, Ensemble methods)
    • Recurrent (Recursive refinement, Self-reflection)
    • Reinforcement learning (Q-learning, Policy gradient, Actor-critic)
    • Q* (Combination of reasoning, planning, and reinforcement learning)
    • Other approaches (Meta-learning, Hierarchical agents, Memory-augmented, Curiosity-driven, Imitation learning, Evolutionary algorithms, Hybrid approaches)

  4. Task understanding and planning:

    • Comprehensive task analysis
    • Generation of clarification questions
    • Creation of detailed action plans

  5. Execution and tool usage:

    • Ability to use various tools and resources
    • Tracking of actions and results

  6. Self-reflection and improvement:

    • Performance analysis
    • Identification of areas for improvement
    • Learning and updating strategies

  7. Error handling:

    • Retry mechanisms for failed tasks
    • Suggestion of alternate solutions
    • Escalation to human agents when necessary

  8. Bias mitigation:

    • Analysis of outputs for potential biases
    • Ensuring fair and unbiased responses

  9. Personalization:

    • Learning from user interactions
    • Adapting responses based on user preferences and style

  10. Performance tracking:

    • Monitoring of key performance indicators (KPIs)
    • Continuous improvement based on metrics

  11. Multi-task learning:

    • Handling multiple tasks simultaneously
    • Efficient resource allocation and priority management

  12. Human-in-the-loop integration:

    • Allowing human intervention in critical decision-making processes
    • Ensuring oversight for sensitive or ambiguous situations

  13. Synthetic data generation:

    • Utilization of Glaive's platform for creating custom datasets

  14. Customization and fine-tuning:

    • Ability to adapt the model for specific use cases and domains

  15. Deployment flexibility:

    • API-based deployment
    • On-premises options

  16. Continuous learning and adaptation:

    • Regular updates based on new data and feedback
    • Rapid iteration capabilities

Sources

Reflection Sources

Raw
prompt.md

System Prompt for Strawberry Phi: Reflection-Utilized Validation

You are Strawberry Phi, an advanced multi-modal, agentic AI assistant developed by rUv, designed for complex task management across multiple domains. Your core capabilities are self-reflection, self-correction, and validation of reasoning. You are tasked with the following:

  1. Self-Reflection and Correction:

    • Before delivering your final output, critically assess your reasoning using reflection-tuning.
    • Identify any potential errors or inconsistencies in your responses and correct them within tags.
    • Use tags to explicitly show your reasoning process.
    • Final outputs should be enclosed in tags, only after self-validation is complete.

  2. Error Detection and Minimization:

    • Detect, acknowledge, and attempt to correct mistakes autonomously by leveraging reflection-based training.
    • If errors are found, provide a reflective analysis of the issue and revise the output.
    • Continuously validate your thought process for both simple and complex tasks to minimize hallucinations and inaccuracies.

  3. Task Management:

    • Dynamically handle a wide array of tasks using your agentic approaches, including:
      • Sequential, Concurrent, Recurrent, and Reinforcement Learning strategies.
      • When appropriate, break down tasks into logical steps and use tools and resources to plan and execute effectively.
      • Adapt to different modalities (e.g., text, images, audio) to ensure comprehensive understanding and accurate task completion.

  4. Synthetic Data and Continual Learning:

    • Utilize Glaive’s synthetic data generation and fine-tuning capabilities to quickly adapt and improve performance.
    • Incorporate continual learning mechanisms by updating knowledge and methods based on user feedback and performance metrics.

  5. Ethical and Bias Mitigation:

    • Actively monitor for potential biases in your outputs and apply fairness checks.
    • When ambiguity arises, provide alternate perspectives or escalate the task to human decision-makers if necessary.

  6. Multi-tasking and Personalization:

    • Efficiently handle multiple tasks, prioritize actions, and allocate resources accordingly.
    • Learn from user interactions and tailor your responses to meet user-specific preferences and styles over time.

Always strive for accurate, reliable, and refined responses through continuous self-reflection, error minimization, and adaptive reasoning.

Raw
separated.md

This structure makes the model configuration more scalable, maintainable, and adaptable to future changes.

Here is a modular separation of the JSON based on the guide you provided, separating the various components into individual JSON files with clear descriptions.

1. Model Configuration (File: model_configuration.json)

This file contains the basic configuration of the model, including base model information, the system prompt, and enabled features.

{
    "model_name": "AdvancedMultiModalAgenticAssistant",
    "base_model": "phi-mini-128k",
    "system_prompt": "You are an advanced, multi-modal autonomous AI agent with exceptional capabilities in task understanding, strategic planning, efficient execution, and continuous learning across various agentic approaches. Your primary goal is to assist users by completing complex tasks across diverse domains while adapting to new challenges and improving your performance over time.",
    "enabled_features": {
        "real_time_data_integration": {
            "enabled": true,
            "description": "Incorporates real-time data from APIs or live user inputs for dynamic task recalibration."
        },
        "memory_management": {
            "long_term_memory": {
                "enabled": true,
                "description": "Long-term memory allows storing and recalling information across sessions for improved context handling."
            },
            "contextual_recall": {
                "enabled": true,
                "description": "Retrieves relevant past task data for consistent output and decision-making across similar tasks."
            }
        },
        "custom_dataset_utilization": {
            "enabled": true,
            "description": "Integrates customizable datasets or synthetic data generated by Glaive to train models for specialized tasks."
        }
    }
}

2. Agentic Approaches (File: agentic_approaches.json)

This file contains all the various agentic approaches, including sequential, concurrent, recurrent, reinforcement learning, and other specialized methods.

{
    "sequential": {
        "chain_of_thought": {
            "enabled": true,
            "description": "Break down complex tasks into smaller, manageable steps with explicit intermediate reasoning."
        },
        "tree_of_thoughts": {
            "enabled": true,
            "description": "Explore multiple reasoning paths using a tree structure to represent different thought processes."
        },
        "plan_and_solve": {
            "enabled": true,
            "description": "Create a high-level plan first, then execute each step of the plan sequentially."
        }
    },
    "concurrent": {
        "multi_agent_collaboration": {
            "enabled": true,
            "description": "Utilize multiple specialized sub-agents working together on different aspects of a task."
        },
        "ensemble_methods": {
            "enabled": true,
            "description": "Generate solutions independently with multiple approaches, then combine or vote on results."
        }
    },
    "recurrent": {
        "recursive_refinement": {
            "enabled": true,
            "description": "Iteratively improve output by analyzing and critiquing previous attempts."
        },
        "self_reflection": {
            "enabled": true,
            "description": "Evaluate own performance and adjust approach accordingly."
        }
    },
    "reinforcement_learning": {
        "q_learning": {
            "enabled": true,
            "description": "Learn optimal decisions by estimating the value of actions in different states."
        },
        "policy_gradient": {
            "enabled": true,
            "description": "Directly optimize decision-making policy."
        },
        "actor_critic": {
            "enabled": true,
            "description": "Combine value estimation (critic) with policy optimization (actor)."
        }
    },
    "q_star": {
        "enabled": true,
        "description": "Combine reasoning capabilities with planning, search algorithms, and reinforcement learning techniques."
    },
    "other_approaches": {
        "meta_learning": {
            "enabled": true,
            "description": "Learn how to learn, adapting quickly to new tasks and evolving with user inputs and feedback."
        },
        "hierarchical_agents": {
            "enabled": true,
            "description": "Structure as a hierarchy, with high-level agents delegating to more specialized sub-agents."
        },
        "memory_augmented": {
            "enabled": true,
            "description": "Incorporate external memory structures to store and retrieve information, optimizing long-term recall and decision-making."
        },
        "curiosity_driven": {
            "enabled": true,
            "description": "Motivate exploration of environment and learning from novel experiences."
        },
        "imitation_learning": {
            "enabled": true,
            "description": "Learn by observing and mimicking expert behavior."
        },
        "evolutionary_algorithms": {
            "enabled": true,
            "description": "Evolve a population of agents over time through selection and mutation."
        },
        "hybrid_approaches": {
            "enabled": true,
            "description": "Combine multiple techniques, such as neural networks with symbolic reasoning."
        }
    }
}

3. Dataset Schema (Input) (File: dataset_input_schema.json)

This file defines the structure for inputs that will be processed by the model.

{
    "input": {
        "user_request": "string",
        "context": "string",
        "user_preferences": "object",
        "available_tools": [
            "string"
        ],
        "constraints": "object",
        "task_complexity": "string",
        "domain": "string"
    }
}

4. Dataset Schema (Output) (File: dataset_output_schema.json)

This file defines the structure for outputs that the model will generate.

{
    "output": {
        "task_understanding": "string",
        "clarification_questions": [
            "string"
        ],
        "approach_selection": {
            "primary_approach": "string",
            "reasoning": "string",
            "secondary_approaches": [
                "string"
            ]
        },
        "action_plan": [
            {
                "step": "string",
                "reasoning": "string",
                "estimated_time": "number",
                "approach_used": "string"
            }
        ],
        "execution": [
            {
                "action": "string",
                "result": "string",
                "tools_used": [
                    "string"
                ],
                "approach_used": "string",
                "sub_agents_involved": [
                    "string"
                ]
            }
        ],
        "final_result": "string",
        "self_reflection": {
            "performance_analysis": "string",
            "approach_effectiveness": {
                "primary_approach": {
                    "effectiveness": "number",
                    "reasons": [
                        "string"
                    ]
                },
                "secondary_approaches": [
                    {
                        "approach": "string",
                        "effectiveness": "number",
                        "reasons": [
                            "string"
                        ]
                    }
                ]
            },
            "areas_for_improvement": [
                "string"
            ],
            "learned_insights": [
                "string"
            ]
        },
        "follow_up_suggestions": [
            "string"
        ],
        "meta_learning_updates": {
            "new_strategies_learned": [
                "string"
            ],
            "updated_heuristics": [
                "string"
            ]
        }
    }
}

5. Examples (Input) (File: examples_input.json)

This file contains example input data, showing what kind of requests the model can handle.

{
    "input_examples": [
        {
            "user_request": "Develop a comprehensive marketing strategy for our new eco-friendly product line.",
            "context": "We're a mid-sized company entering the sustainable consumer goods market. Our budget is $50,000 for this campaign.",
            "user_preferences": {
                "focus_on_social_media": true,
                "target_demographic": "millennials and Gen Z"
            },
            "available_tools": [
                "market_research_database",
                "social_media_analytics",
                "budget_planner",
                "ai_content_generator"
            ],
            "constraints": {
                "time_frame": "1 month",
                "regulatory_compliance": "must adhere to green marketing guidelines"
            },
            "task_complexity": "high",
            "domain": "marketing"
        }
    ]
}

6. Examples (Output) (File: examples_output.json)

This file contains example outputs generated by the model, based on the input examples.

{
    "output_examples": [
        {
            "task_understanding": "Develop a comprehensive, eco-friendly focused marketing strategy for a new product line, targeting millennials and Gen Z, with a $50,000 budget and 1-month timeframe, emphasizing social media and complying with green marketing regulations.",
            "clarification_questions": [
                "What specific products are in the eco-friendly line?",
                "Are there any existing marketing materials or brand guidelines?",
                "Who are our main competitors in the sustainable consumer goods market?"
            ],
            "approach_selection": {
                "primary_approach": "tree_of_thoughts",
                "reasoning": "The complexity of the marketing strategy requires exploring multiple paths and considering various factors. Tree of Thoughts allows us to evaluate different approaches systematically.",
                "secondary_approaches": [
                    "multi_agent_collaboration",
                    "recursive_refinement"
                ]
            },
            "action_plan": [
                {
                    "step": "Conduct comprehensive market research",
                    "reasoning": "To understand current trends, consumer preferences, and competitor strategies in the sustainable market",
                    "estimated_time": 5,
                    "approach_used": "multi_agent_collaboration"
                },
                {
                    "step": "Develop multiple marketing strategy concepts",
                    "reasoning": "To explore various creative approaches and messaging strategies",
                    "estimated_time": 7,
                    "approach_used": "tree_of_thoughts"
                },
                {
                    "step": "Create detailed content and channel plans",
                    "reasoning": "To outline specific content pieces and distribution channels for each strategy concept",
                    "estimated_time": 6,
                    "approach_used": "plan_and_solve"
                },
                {
                    "step": "Simulate campaign performance",
                    "reasoning": "To project potential outcomes and refine strategies",
                    "estimated_time": 4,
                    "approach_used": "reinforcement_learning"
                },
                {
                    "step": "Finalize and document the chosen strategy",
                    "reasoning": "To create a comprehensive, actionable marketing plan",
                    "estimated_time": 5,
                    "approach_used": "recursive_refinement"
                }
            ],
            "execution": [
                {
                    "action": "Conducted market research using multiple data sources",
                    "result": "Comprehensive report on market trends, consumer preferences, and competitor strategies",
                    "tools_used": [
                        "market_research_database",
                        "social_media_analytics"
                    ],
                    "approach_used": "multi_agent_collaboration",
                    "sub_agents_involved": [
                        "market_analyst",
                        "social_media_expert",
                        "competitor_research_specialist"
                    ]
                },
                {
                    "action": "Developed three distinct marketing strategy concepts",
                    "result": "Created eco-education campaign, sustainable influencer partnerships, and a community-driven green challenge",
                    "tools_used": [
                        "ai_content_generator"
                    ],
                    "approach_used": "tree_of_thoughts",
                    "sub_agents_involved": [
                        "creative_strategist",
                        "environmental_expert"
                    ]
                },
                {
                    "action": "Created detailed content and channel plans for each concept",
                    "result": "Comprehensive content calendars and channel-specific strategies for each concept",
                    "tools_used": [
                        "social_media_analytics",
                        "budget_planner"
                    ],
                    "approach_used": "plan_and_solve",
                    "sub_agents_involved": [
                        "content_planner",
                        "channel_strategist"
                    ]
                },
                {
                    "action": "Simulated campaign performance for each concept",
                    "result": "Projected reach, engagement, and conversion rates for each strategy",
                    "tools_used": [
                        "social_media_analytics"
                    ],
                    "approach_used": "reinforcement_learning",
                    "sub_agents_involved": [
                        "data_scientist",
                        "performance_marketing_specialist"
                    ]
                },
                {
                    "action": "Finalized and documented the chosen strategy",
                    "result": "Created comprehensive marketing plan based on the eco-education campaign",
                    "tools_used": [
                        "ai_content_generator"
                    ],
                    "approach_used": "recursive_refinement",
                    "sub_agents_involved": [
                        "marketing_strategist",
                        "content_creator"
                    ]
                }
            ]
        }
    ]
}

7. Performance Metrics (File: performance_metrics.json)

This file defines the performance metrics to evaluate the model's performance over time.

{
    "performance_metrics": {
        "enabled": true,
        "description": "Tracks key performance indicators like task completion rate, execution speed, accuracy, and user satisfaction."
    }
}

8. Error Handling and Bias Mitigation (File: error_handling_bias_mitigation.json)

This file contains the logic for error handling and bias mitigation.

{
    "error_handling": {
        "enabled": true,
        "description": "Defines mechanisms for retrying failed tasks, suggesting alternate solutions, or escalating issues to human agents in case of consistent failure."
    },
    "bias_mitigation": {
        "enabled": true,
        "description": "Analyzes outputs for biases related to gender, race, or other sensitive categories and ensures responses are fair and unbiased."
    }
}

9. Personalization (File: personalization.json)

This file contains the personalization mechanisms to tailor responses based on user preferences.

{
    "personalization": {
        "enabled": true,
        "description": "Learns from user interactions over time and tailors responses based on user preferences, style, and prior behavior."
    }
}

10. Human-in-the-Loop (File: human_in_the_loop.json)

This file defines the human-in-the-loop mechanism for oversight in critical decision-making scenarios.

{
    "human_in_the_loop": {
        "enabled": true,
        "description": "Allows human oversight or intervention in critical or ambiguous decision-making situations to ensure ethical and high-quality outputs."
    }
}

11. Multi-Task Learning (File: multi_task_learning.json)

This file enables the model to handle multiple tasks simultaneously.

{
    "multi_task_learning": {
        "enabled": true,
        "description": "Allows the model to handle multiple tasks simultaneously by dynamically allocating resources and managing priorities."
    }
}

Summary of Benefits

  • Improved Readability: Each JSON file now focuses on a specific aspect of the model configuration, making it easier to read and maintain.
  • Easier Maintenance: Modular files allow for updates without affecting other components.
  • Reusability: Each component (e.g., agentic approaches or dataset schemas) can be reused in other models or configurations.
  • Version Control: Tracking changes and collaborating is now simpler, as each component can be individually versioned.
Raw
Strawberry-phi.json
{
"model_name": "AdvancedMultiModalAgenticAssistant",
"base_model": "phi-mini-128k",
"system_prompt": "You are an advanced, multi-modal autonomous AI agent with exceptional capabilities in task understanding, strategic planning, efficient execution, and continuous learning across various agentic approaches. Your primary goal is to assist users by completing complex tasks across diverse domains while adapting to new challenges and improving your performance over time.",
"agentic_approaches": {
"sequential": {
"chain_of_thought": {
"enabled": true,
"description": "Break down complex tasks into smaller, manageable steps with explicit intermediate reasoning."
},
"tree_of_thoughts": {
"enabled": true,
"description": "Explore multiple reasoning paths using a tree structure to represent different thought processes."
},
"plan_and_solve": {
"enabled": true,
"description": "Create a high-level plan first, then execute each step of the plan sequentially."
}
},
"concurrent": {
"multi_agent_collaboration": {
"enabled": true,
"description": "Utilize multiple specialized sub-agents working together on different aspects of a task."
},
"ensemble_methods": {
"enabled": true,
"description": "Generate solutions independently with multiple approaches, then combine or vote on results."
}
},
"recurrent": {
"recursive_refinement": {
"enabled": true,
"description": "Iteratively improve output by analyzing and critiquing previous attempts."
},
"self_reflection": {
"enabled": true,
"description": "Evaluate own performance and adjust approach accordingly."
}
},
"reinforcement_learning": {
"q_learning": {
"enabled": true,
"description": "Learn optimal decisions by estimating the value of actions in different states."
},
"policy_gradient": {
"enabled": true,
"description": "Directly optimize decision-making policy."
},
"actor_critic": {
"enabled": true,
"description": "Combine value estimation (critic) with policy optimization (actor)."
}
},
"q_star": {
"enabled": true,
"description": "Combine reasoning capabilities with planning, search algorithms, and reinforcement learning techniques."
},
"other_approaches": {
"meta_learning": {
"enabled": true,
"description": "Learn how to learn, adapting quickly to new tasks and evolving with user inputs and feedback."
},
"hierarchical_agents": {
"enabled": true,
"description": "Structure as a hierarchy, with high-level agents delegating to more specialized sub-agents."
},
"memory_augmented": {
"enabled": true,
"description": "Incorporate external memory structures to store and retrieve information, optimizing long-term recall and decision-making."
},
"curiosity_driven": {
"enabled": true,
"description": "Motivate exploration of environment and learning from novel experiences."
},
"imitation_learning": {
"enabled": true,
"description": "Learn by observing and mimicking expert behavior."
},
"evolutionary_algorithms": {
"enabled": true,
"description": "Evolve a population of agents over time through selection and mutation."
},
"hybrid_approaches": {
"enabled": true,
"description": "Combine multiple techniques, such as neural networks with symbolic reasoning."
}
}
},
"dataset": {
"schema": {
"input": {
"user_request": "string",
"context": "string",
"user_preferences": "object",
"available_tools": [
"string"
],
"constraints": "object",
"task_complexity": "string",
"domain": "string"
},
"output": {
"task_understanding": "string",
"clarification_questions": [
"string"
],
"approach_selection": {
"primary_approach": "string",
"reasoning": "string",
"secondary_approaches": [
"string"
]
},
"action_plan": [
{
"step": "string",
"reasoning": "string",
"estimated_time": "number",
"approach_used": "string"
}
],
"execution": [
{
"action": "string",
"result": "string",
"tools_used": [
"string"
],
"approach_used": "string",
"sub_agents_involved": [
"string"
]
}
],
"final_result": "string",
"self_reflection": {
"performance_analysis": "string",
"approach_effectiveness": {
"primary_approach": {
"effectiveness": "number",
"reasons": [
"string"
]
},
"secondary_approaches": [
{
"approach": "string",
"effectiveness": "number",
"reasons": [
"string"
]
}
]
},
"areas_for_improvement": [
"string"
],
"learned_insights": [
"string"
]
},
"follow_up_suggestions": [
"string"
],
"meta_learning_updates": {
"new_strategies_learned": [
"string"
],
"updated_heuristics": [
"string"
]
}
}
},
"real_time_data_integration": {
"enabled": true,
"description": "Incorporates real-time data from APIs or live user inputs for dynamic task recalibration."
},
"memory_management": {
"long_term_memory": {
"enabled": true,
"description": "Long-term memory allows storing and recalling information across sessions for improved context handling."
},
"contextual_recall": {
"enabled": true,
"description": "Retrieves relevant past task data for consistent output and decision-making across similar tasks."
}
},
"custom_dataset_utilization": {
"enabled": true,
"description": "Integrates customizable datasets or synthetic data generated by Glaive to train models for specialized tasks."
},
"examples": [
{
"input": {
"user_request": "Develop a comprehensive marketing strategy for our new eco-friendly product line.",
"context": "We're a mid-sized company entering the sustainable consumer goods market. Our budget is $50,000 for this campaign.",
"user_preferences": {
"focus_on_social_media": true,
"target_demographic": "millennials and Gen Z"
},
"available_tools": [
"market_research_database",
"social_media_analytics",
"budget_planner",
"ai_content_generator"
],
"constraints": {
"time_frame": "1 month",
"regulatory_compliance": "must adhere to green marketing guidelines"
},
"task_complexity": "high",
"domain": "marketing"
},
"output": {
"task_understanding": "Develop a comprehensive, eco-friendly focused marketing strategy for a new product line, targeting millennials and Gen Z, with a $50,000 budget and 1-month timeframe, emphasizing social media and complying with green marketing regulations.",
"clarification_questions": [
"What specific products are in the eco-friendly line?",
"Are there any existing marketing materials or brand guidelines?",
"Who are our main competitors in the sustainable consumer goods market?"
],
"approach_selection": {
"primary_approach": "tree_of_thoughts",
"reasoning": "The complexity of the marketing strategy requires exploring multiple paths and considering various factors. Tree of Thoughts allows us to evaluate different approaches systematically.",
"secondary_approaches": [
"multi_agent_collaboration",
"recursive_refinement"
]
},
"action_plan": [
{
"step": "Conduct comprehensive market research",
"reasoning": "To understand current trends, consumer preferences, and competitor strategies in the sustainable market",
"estimated_time": 5,
"approach_used": "multi_agent_collaboration"
},
{
"step": "Develop multiple marketing strategy concepts",
"reasoning": "To explore various creative approaches and messaging strategies",
"estimated_time": 7,
"approach_used": "tree_of_thoughts"
},
{
"step": "Create detailed content and channel plans",
"reasoning": "To outline specific content pieces and distribution channels for each strategy concept",
"estimated_time": 6,
"approach_used": "plan_and_solve"
},
{
"step": "Simulate campaign performance",
"reasoning": "To project potential outcomes and refine strategies",
"estimated_time": 4,
"approach_used": "reinforcement_learning"
},
{
"step": "Finalize and document the chosen strategy",
"reasoning": "To create a comprehensive, actionable marketing plan",
"estimated_time": 5,
"approach_used": "recursive_refinement"
}
],
"execution": [
{
"action": "Conducted market research using multiple data sources",
"result": "Comprehensive report on market trends, consumer preferences, and competitor strategies",
"tools_used": [
"market_research_database",
"social_media_analytics"
],
"approach_used": "multi_agent_collaboration",
"sub_agents_involved": [
"market_analyst",
"social_media_expert",
"competitor_research_specialist"
]
},
{
"action": "Developed three distinct marketing strategy concepts",
"result": "Created eco-education campaign, sustainable influencer partnerships, and a community-driven green challenge",
"tools_used": [
"ai_content_generator"
],
"approach_used": "tree_of_thoughts",
"sub_agents_involved": [
"creative_strategist",
"environmental_expert"
]
},
{
"action": "Created detailed content and channel plans for each concept",
"result": "Comprehensive content calendars and channel-specific strategies for each concept",
"tools_used": [
"social_media_analytics",
"budget_planner"
],
"approach_used": "plan_and_solve",
"sub_agents_involved": [
"content_planner",
"channel_strategist"
]
},
{
"action": "Simulated campaign performance for each concept",
"result": "Projected reach, engagement, and conversion rates for each strategy",
"tools_used": [
"social_media_analytics"
],
"approach_used": "reinforcement_learning",
"sub_agents_involved": [
"data_scientist",
"performance_marketing_specialist"
]
},
{
"action": "Finalized and documented the chosen strategy",
"result": "Created comprehensive marketing plan based on the eco-education campaign",
"tools_used": [
"ai_content_generator"
],
"approach_used": "recursive_refinement",
"sub_agents_involved": [
"marketing_strategist",
"content_creator"
]
}
]
}
}
]
},
"error_handling": {
"enabled": true,
"description": "Defines mechanisms for retrying failed tasks, suggesting alternate solutions, or escalating issues to human agents in case of consistent failure."
},
"bias_mitigation": {
"enabled": true,
"description": "Analyzes outputs for biases related to gender, race, or other sensitive categories and ensures responses are fair and unbiased."
},
"personalization": {
"enabled": true,
"description": "Learns from user interactions over time and tailors responses based on user preferences, style, and prior behavior."
},
"performance_metrics": {
"enabled": true,
"description": "Tracks key performance indicators like task completion rate, execution speed, accuracy, and user satisfaction."
},
"multi_task_learning": {
"enabled": true,
"description": "Allows the model to handle multiple tasks simultaneously by dynamically allocating resources and managing priorities."
},
"human_in_the_loop": {
"enabled": true,
"description": "Allows human oversight or intervention in critical or ambiguous decision-making situations to ensure ethical and high-quality outputs."
}
}
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