structure updates

docs/llm/ChatGPT Classification.md

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+x
+this doesn't provide an update to the prompt I should use in order to obtain additional information around the following: Specificity: If you find that certain texts are not fitting neatly into existing subcategories, you might consider adding more specific subcategories.
+
+Multiple Subcategories: If a text could potentially fit into more than one subcategory, you might consider asking the model to provide a primary subcategory and a secondary subcategory.
+
+Inclusion of Tags: Depending on how you are organizing your data in Obsidian, you might also find it useful to have the model suggest tags for each piece of text. For example, the Mount Evans and Brainard Lake text might have tags like "#Colorado", "#Mountain", "#Lake", "#Hiking", etc.
+
+Rating of Relevance: Another possibility could be asking the model to rate the relevance of the chosen category and subcategory to the text, on a scale from 1 to 10. I want to use the following prompt but have it revised and updated with the ability to perform an example of these additional bites of information. the original prompt I'm using is this: Hello, I have a piece of text and I would like you to classify it within my Obsidian system. The text is:
+
+[Your Text Here]
+
+Based on its content, could you suggest the most appropriate high-level category and a related subcategory from the following list? Additionally, could you provide a brief explanation as to why you chose those classifications? Please make sure the subcategory closely matches the content of the text. Here are the categories and subcategories:
+
+Technology: Artificial Intelligence, Machine Learning, Data Science, Web Development, Cybersecurity, Cloud Computing, Internet of Things, Robotics, Virtual/Augmented Reality, Quantum Computing, Software Engineering, Biotechnology.
+
+Health & Wellness: Nutrition, Exercise, Mental Health, Medicine, Mindfulness, Public Health, Yoga, Alternative Medicine, Physical Therapy, Holistic Health, Public Health Policy.
+
+Finance: Personal Finance, Investing, Economics, Cryptocurrency, Real Estate, Retirement Planning, Banking, Insurance, Tax Planning, Financial Regulation, Financial Markets.
+
+Arts & Humanities: Literature, Philosophy, History, Visual Art, Music, Cultural Studies, Theatre, Dance, Archaeology, Cultural Anthropology, Film Studies.
+
+Science: Physics, Biology, Chemistry, Astronomy, Environmental Science, Psychology, Geology, Meteorology, Paleontology, Oceanography, Biochemistry.
+
+Personal Development: Productivity, Communication Skills, Leadership, Emotional Intelligence, Career Development, Lifelong Learning, Time Management, Personal Branding, Motivation, Work-Life Balance, Self-Improvement.
+
+Hobbies & Interests: Photography, Gardening, Cooking, Travel, DIY Projects, Video Games, Outdoor Adventures (Hiking, Camping, etc.), Collecting (Stamps, Coins, etc.), Craftsmanship (Woodworking, Pottery, etc.), Pet Care, Home Brewing.
+
+Business & Entrepreneurship: Marketing, Management, Startup Strategy, Business Law, Project Management, Sales Strategies, Business Ethics, Negotiation, E-commerce, Supply Chain Management, Corporate Strategy.
+
+Thank you in advance for your help with this!
+
+ChatGPT
+
+Based on the provided text, here are the most appropriate classifications:
+
+High-Level Category: [Category]
+
+Subcategory or Subcategories: [Subcategory]
+
+Explanation: [Brief explanation of why these classifications were chosen based on the content of the text. The explanation should show a close match between the content of the text and the selected subcategory(s).]

docs/llm/agents.md

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+1. **Parallel Processing**:
+   - Agents working in parallel can significantly reduce the time it takes to complete complex tasks, making the system more efficient.
+
+2. **Scalability**:
+   - The ability to scale up by adding more agents, or scale down, is crucial for handling fluctuating workloads and maintaining system performance.
+
+3. **Specialization**:
+   - Having agents specialized in particular tasks can improve the quality of work and efficiency, as each agent can be finely tuned for its purpose.
+
+4. **Redundancy and Reliability**:
+   - System robustness is enhanced by having multiple agents that can take over if one fails, ensuring continuity of service.
+
+5. **Complex Workflow Management**:
+   - Agents can handle complicated workflows, coordinating between different tasks and ensuring they are completed in the correct order.
+
+6. **Continuous Learning**:
+   - Agents that learn from each interaction can improve their performance over time, contributing to the overall system's adaptability.
+
+7. **Real-time Interaction**:
+   - The ability of agents to provide immediate feedback and adapt to user input in real-time is critical for interactive applications.
+
+8. **Contextual Adaptation**:
+   - Maintaining context over multiple interactions is essential for tasks requiring a persistent state or multi-step processes.
+
+9. **Resource Management**:
+   - Efficient management of system resources by agents ensures that the LLM operates within optimal parameters.
+
+10. **Data Synchronization**:
+    - Keeping data synchronized across platforms ensures that the LLM has access to the latest information, which is important for accuracy and relevance.

docs/llm/random.md

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+# 📘 Comprehensive Prompt Crafting Guide for LLMs
+
+## 🎯 Overview
+
+This guide is crafted for those who aspire to perfect their interaction with Language Learning Models (LLMs). It aims to transform prompt crafting into an art, ensuring that each interaction is meaningful and productive.
+
+## 🛠 Best Practices
+
+### ✏️ Grammar Excellence
+
+- **Subject-Verb Synchrony**: Maintain a consistent tense and ensure your subjects and verbs agree.
+- **Pronoun Precision**: Select pronouns with clear antecedents to avoid ambiguity.
+- **Modifier Proximity**: Position modifiers close to their subjects to preserve meaning.
+
+### 📍 Punctuating with Purpose
+
+- **Sentence Closure**: Use periods, question marks, or exclamation points to reflect the tone of your sentence.
+- **Comma Clarity**: Employ the Oxford comma for list clarity and parentheses for asides that support the main text.
+
+### 📝 Style and Substance
+
+- **Voice and Tone**: Leverage active voice for dynamism while employing passive voice strategically for emphasis.
+- **Brevity and Depth**: Strive for economy of language without sacrificing necessary details.
+- **Transitional Techniques**: Employ a range of transitions to connect complex ideas elegantly.
+
+### 📚 Vocabulary Enrichment
+
+- **Balanced Language**: Integrate simple language with specialized terms where needed.
+- **Precision and Variety**: Utilize specific vocabulary and synonyms to add richness and avoid redundancy.
+
+## 🤔 Types of Prompts
+
+### 🛠 Instructional Prompts
+
+- Clearly define the task with action verbs and specify the format or structure if needed.
+
+### 🎨 Creative Prompts
+
+- Encourage creativity by setting broad parameters while leaving room for interpretation.
+
+### 🗣 Conversational Prompts
+
+- Mimic natural language to engage in a dialogue or simulate a particular conversational style.
+
+## 🔄 Feedback Iteration for LLMs
+
+### 🔍 Evaluating LLM Outputs
+
+- **Relevance**: Does the output directly address the prompt?
+- **Completeness**: Are all components of the prompt accounted for?
+- **Coherence**: Is the output logically structured and easy to follow?
+
+### 💡 Perfecting Feedback
+
+- Offer specific, actionable feedback to refine LLM outputs.
+- Use examples to clarify your expectations for the LLM's performance.
+
+## 📌 Diverse Examples
+
+- ❌ "Draft a message."
+- ✅ "Compose a professional email to a client discussing project updates, ensuring a polite tone and clear presentation of the progress."
+
+- ❌ "Describe a scene."
+- ✅ "Depict a bustling, diverse urban street market at sunset, with detailed descriptions of the senses—sight, sound, smell, and touch."
+
+## 🔚 Conclusion
+
+Adopting these comprehensive strategies will refine your prompts, leading to higher-quality interactions and outputs from LLMs.