NovoMindAI

Tables

Blinkist

Knowledge Base Links

UX of NovoMind.ai App V1

How to Build An AI Agent Team in ChatGPT

Considerations when Building the template

Draft memory Approach

Untitled page

Explore

UX of NovoMind.ai App V1

To enhance the NovaMind AI app's workflow, we can integrate additional features into each step, focusing on both user experience (UX) and your administrative journey. Here's a refined approach:

Step 1: Define Learning Objectives and Relevance

User Action: Users articulate their learning goals within the app.

App Function:

Implement an intuitive form with prompts like “What skill are you aiming to acquire?” and “Why is this important to you?”

Introduce a progress tracker to monitor goal completion.

Outcome: Establishes a personalized learning path, stored in the user’s profile.

Value: Ensures content recommendations are aligned with user aspirations.

Step 2: Upload Learning Profile and Qualifications

User Action: Users provide details about their qualifications and skills.

App Function:

Enable uploads of documents like CVs and certificates.

Incorporate a skills assessment to identify strengths and areas for improvement.

AI Integration: Utilize AI to analyze profiles and tailor content recommendations.

Value: Deepens personalization by aligning content with the user’s background.

Step 3: AI Analysis and Video Recommendations

User Action: Users receive curated video suggestions.

App Function:

AI analyzes available content to match user profiles.

Display recommendations with brief descriptions and estimated completion times.

Affiliate Opportunities: Partner with educational platforms to offer affiliate links.

Outcome: Users access a tailored list of videos.

Value: Streamlines the discovery of relevant learning materials.

Notes:

Use Perplexity as of 4 Nov: Perplexity is better than ChatGPT Search at this task. Perplexity could find any YouTube Video. Open AI struggled with new videos or it could be videos that did not allow sharing of the transcript

Can not use CustomGPT: This will be the cases unless the existing CustomGPT websearch features are improved

Step 4: Generate Report with Recommendations and Suggested Questions

User Action: Users review a detailed report for each recommended video.

App Function:

AI generates reports explaining the relevance of each video.

Include potential questions to guide focus during viewing.

Embed affiliate links for additional resources.

Outcome: Users make informed decisions about which videos to watch.

Value: Enhances understanding of content relevance and offers monetization avenues.

Notes: See above: Needs done in Perplexity

Step 5: Call on Output Agent to generate structured output of NovaMindAI App.

4.1 User is asked if they want to refine Questions and Set Actionable Goals

If Yes, do this steps, if no got to 4.2

User Action: As an option, the User calls on SMART Goal Agent who helps review and adjust AI-generated questions, then establish specific, actionable goals.

App Function:

Guided Refinement: Provide clear instructions within ChatGPT, prompting users to assess and modify the AI-generated questions to better align with their learning objectives.

SMART Goal Framework: Encourage users to formulate goals using the SMART criteria:

Specific: Clearly define the goal.

Measurable: Ensure the goal's progress can be tracked.

Achievable: Set realistic and attainable goals.

Relevant: Align the goal with broader learning objectives.

Time-bound: Specify a deadline for achieving the goal.

Examples and Templates: Offer sample goals and templates to assist users in crafting their own. For instance:

Example Goal: "Complete the 'Introduction to Data Science' course on Coursera by December 31, 2024, to enhance data analysis skills."

Template: "I will [Specific Action] by [Timeframe] to achieve [Desired Outcome]."

Value: This step promotes focused and measurable learning outcomes, ensuring users have clear objectives and a structured plan to achieve them.

4.2 Download Markdown File of this Chat into the download folder

Step 6: Go to

NovoMindAI.com⁠

platform and upload the Markdown file

Open the

NovoMindAI.com⁠

app and upload the Markdown file from the download folder

The app turns the markdown fil into the mentor template

NovoMind AI User Interface (UI)

Designing an intuitive and user-friendly interface for the NovoMind app is crucial to enhance the learning experience. Here's a structured overview of the proposed interface, aligning with the previously outlined user experience (UX):

1. Dashboard

Overview: Upon logging in, users are greeted with a personalized dashboard displaying their learning objectives, progress, and upcoming tasks.

Key Features:

Learning Objectives: A section highlighting current goals with options to add or modify them.

Progress Tracker: Visual indicators (e.g., progress bars) showing advancement in various learning modules.

Upcoming Tasks: A list of pending activities or recommended content based on user preferences.

2. Learning Profile

Overview: A dedicated area where users can input and update their qualifications, skills, and previous learning experiences.

Key Features:

Profile Information: Fields for educational background, certifications, and skill sets.

Learning History: A log of completed courses, watched videos, and achieved milestones.

Edit Options: Easy-to-use forms for updating personal information and learning preferences.

3. Content Discovery

Overview: A prompt template that can be run in Perplexity or ChatGPT search recommending videos and articles tailored to the user's learning objectives and profile.

Key Features:

Search Functionality: Enter the objective of the session in a search bar allowing users.

Filters and Categories: Options to sort content by relevance, difficulty level, or subject matter and the preferred AI search engine (ChatGPT/Perplexity/You.com)

Copy-paste Prompt: A button copies Prompt and opens the preferered AI Search Engine

Recommendations: AI Search Engine-driven suggestions based on user behavior and stated goals.

4. Interactive Learning Modules

Overview: Sections where users can engage with content, take notes, and participate in quizzes.

Key Features:

Content Upload: Users can upload Markdown files containing text summaries, which the system processes using regex commands to extract key information into structured table rows.

Video Integration: A dedicated field for inputting video URLs, allowing seamless embedding and playback within the module.

Section Breakdown: The system divides content into sections, each with fields for Questions, Key Notes, and Actions, facilitating organized learning.

Additional Formats: Support for uploading PDFs, Word documents, and audio files, expanding the range of learning materials.

5. Mentoring and AI Assistance

Overview: Access to AI-driven mentoring agents providing guidance, answering questions, and offering feedback.

Key Features:

Chat Interface: Real-time messaging with AI mentors simulating expert interactions.

Voice Assistance: Optional voice-based interactions for a more immersive experience.

Personalized Feedback: Tailored advice and suggestions based on user progress and performance.

6. Progress and Analytics

Overview: Comprehensive analytics dashboard displaying detailed insights into learning patterns and achievements.

Key Features:

Performance Metrics: Charts and graphs illustrating quiz scores, completion rates, and time spent on various modules.

Goal Tracking: Tools to set, monitor, and adjust learning objectives.

Export Options: Ability to download reports or share progress with mentors or peers.

7. Community and Collaboration on Systeme

Overview: A platform for users to ask questions, provide feedback, share knowledge, and collaborate on areas.

Key Features:

Discussion Forums: Themed forums for topic-specific suggestions and Q&A sessions.

Expert Groups: Spaces to collaborate on topic areas.

Peer Feedback: Mechanisms to review and provide constructive feedback on each other's work.

8. Settings and Customization

Overview: Options for users to personalize their experience and manage account settings.

Key Features:

Profile Customization: Themes, notification preferences, and display settings.

Security Settings: Password management, two-factor authentication, and privacy controls.

Integration Options: Connections to external tools like note-taking apps or calendars.

By incorporating these elements, the NovoMind app aims to provide a holistic and engaging learning environment, leveraging AI to personalize and enhance the educational journey

Step 7: Watch Video (Read/edit files and listen to audio files ) and Take Notes

User Action: Users view the selected video and document notes.

App Function:

Integrate with note-taking apps like Notion or Evernote.

Enable timestamped notes linked to specific video segments.

Offer organizational tools such as tags and folders.

Affiliate Opportunities: Recommend premium note-taking tools through affiliate links.

Outcome: Users maintain organized and accessible notes.

Value: Facilitates effective information retention and organization.

Step 8: Reflect and Set Goals (Post-Watch)

User Action: Users reflect on their learning and establish future goals.

App Function:

Provide guided reflection prompts to assess understanding.

Set reminders for follow-up actions or reviews.

Allow users to revisit and adjust their learning objectives.

Outcome: Users reinforce their learning and plan subsequent steps.

Value: Encourages continuous improvement and application of knowledge.

Administrative Journey

User Data Management: Access dashboards to monitor user progress and engagement.

Content Curation: Utilize AI analytics to identify trending topics and update the content library accordingly.

Affiliate Management: Track affiliate link performance and manage partnerships.

Feedback Collection: Implement surveys to gather user feedback for ongoing app enhancement.

By integrating these features, the NovoMind AI app can offer a comprehensive and personalized learning experience, fostering user engagement and facilitating effective knowledge acquisition.

Mentoring

Integrating a mentoring component into the NovoMind AI app can significantly enhance the learning experience by providing structured guidance and personalized support. Here's how we can implement this:

1. Extensive Video Outline with Objectives and Key Tasks

AI-Generated Outlines: Utilize AI to analyze selected videos and generate comprehensive outlines, detailing objectives, context, key tasks, and expected outcomes. This provides users with a clear roadmap of the content, facilitating better understanding and retention.

User Interaction: Allow users to review and customize these outlines, aligning them with their personal learning goals. This customization ensures that the learning process remains relevant and targeted.

2. Personalized Mentoring via GPT

User Profile Integration: Develop a detailed user profile encompassing learning objectives, progress, and preferences. This profile enables the GPT model to tailor its mentoring approach effectively.

Interactive Mentoring Sessions: Leverage GPT's capabilities to conduct mentoring sessions based on the video outlines. The AI can discuss strategies to achieve set goals, provide feedback on tasks, and suggest resources for further learning.

Voice Feature Utilization: Incorporate GPT's voice capabilities to facilitate natural, conversational interactions during mentoring sessions. This approach enhances engagement and simulates real-life mentoring experiences.

⁠

OpenAI⁠

⁠

3. Integration with Platform Database

Data Synchronization: Ensure that key points from mentoring discussions are automatically updated in the platform's database. This integration allows for tracking progress and informs future interactions, creating a cohesive learning journey.

Continuous Learning Loop: Utilize the stored data to refine AI recommendations, adapting to the user's evolving needs and ensuring that the mentoring remains relevant and effective.

4. Utilizing GPT Canvas for Summarization and Discussion

Bullet Point Summaries: Employ GPT Canvas to generate concise bullet point summaries of video content. These summaries can serve as focal points for mentoring discussions, ensuring that key concepts are thoroughly understood.

⁠

OpenAI⁠

⁠

Parallel Voice Discussions: Implement a feature where GPT provides voice-based explanations for each bullet point. This dual-mode communication caters to different learning preferences and reinforces comprehension.

Considerations for Implementation

Technical Feasibility: Assess the current capabilities of GPT Canvas and its compatibility with voice features. If existing tools do not support this functionality, consider developing a custom solution to meet these requirements.

User Experience: Design the mentoring feature to be intuitive and user-friendly, ensuring that users can easily navigate between video content, outlines, and mentoring sessions.

Privacy and Security: Implement robust data protection measures to safeguard user information, especially given the personalized nature of mentoring sessions.

By integrating these features, the LEARN-AI app can offer a comprehensive mentoring experience that not only guides users through their learning journey but also adapts to their individual needs and preferences.

Sources

You said:

First steps in this process may be to find away to doa suryve and fintine these ideas. Lets consider this tomorrow. Thanks and good night

ChatGPT said:

ChatGPT

You're welcome! Conducting a survey to refine these ideas is a prudent next step. Utilizing tools like Google Forms or SurveyMonkey can facilitate efficient data collection and analysis. Additionally, platforms such as OpinionX offer specialized features for idea validation, allowing you to rank and segment customer preferences effectively.

⁠

OpinionX⁠

⁠

When designing your survey, consider including questions that assess:

User Interest: Gauge the level of interest in personalized learning experiences and AI-driven mentoring.

Feature Preferences: Identify which features (e.g., interactive quizzes, personalized mentoring plans, gamification elements) are most appealing to potential users.

Platform Usability: Understand user expectations regarding platform navigation, integration with existing tools, and overall user experience.

Willingness to Pay: Determine if users are willing to invest in such a platform and at what price point.

By gathering insights through a well-structured survey, you can make informed decisions that align with your target audience's needs and preferences.

Have a good night, and we can delve deeper into this tomorrow.

Sources

You said:

I have through about some more ideas about this Learn AI platform. Please review your second last response and add these ideas: I am considering three interrelated digital products initially. Product 1) I will have a UI the has the Learn AI interface. The MVP will be a free standing Coda Template, which can be linked through Make at a later date I will call the Learn with AI Doc. ($20). This Doc will generate prompts for ChatGPT to take advantage of ChatGPT Search (possible Perplexity.ai or You.com or GEMS in GEMINI.ai) Product 2) I will create Make Automations that can link up the product to ChatGPT (Also possibly GEMS or Hugging Face later). Product 3) To support this platform the next produce , I will create is LEARN Agents. (An additional feature that these Agents will send data to Users LEARN with AI Doc database). The LEARN Agents will operate as follows # Custom GPT process: I'll provide a detailed, LLM-friendly summary of the video that focuses on actionable insights and clear processes. Video Title: "How to Build An AI Agent Team in ChatGPT" Source Reference: [1, {ts:0.04}] Core Concept Overview: The video introduces ChatGPT's "@" feature that enables users to create workflows using multiple custom GPTs as interconnected agents. Key Components and Architecture: 1. Agent Army Framework: - Primary Function: Create workflows connecting multiple specialized GPTs - Implementation Method: Using ChatGPT's "@" mention feature - Purpose: Automate business tasks through interconnected AI agents 2. Integration Components: [1, {ts:249.56}] The system utilizes: - Custom actions integration - Custom code implementation - Make/Zapier connections - CRM bridge connections - Day-to-day tool integration capabilities 3. Core Technical Features: - Webhook implementation - API key management - Replit connections - Data handling protocols - Security measures Implementation Process: 1. Agent Creation Phase: - Define specific agent roles - Configure custom GPT settings - Establish connection protocols - Set up authentication methods 2. Workflow Integration: - Connect multiple agents using "@" feature - Establish communication protocols between agents - Define task handoff procedures - Implement error handling mechanisms 3. Tool Integration Framework: - Make/OpenAI integration - Airtable connection setup - OpenRouter implementation - Pinecone database integration Specific Agent Examples: 1. Repet Agent & Pinecone Assistant: [1, {ts:294.96}] Features: - Enhanced accuracy in responses - Knowledge base integration - Data retrieval capabilities - Context maintenance 2. Auto-Proposal Generator: - Make integration - Template management - Automated document creation - Customization capabilities 3. CRM Integration: - Airtable contact logging - Database management - Client information tracking - Automated updates 4. Multi-LLM Access: - OpenRouter integration - Multiple model access - Response optimization - Model selection logic System Requirements: 1. Technical Prerequisites: - API access credentials - Webhook endpoints - Database connections - Integration permissions 2. Configuration Requirements: - Security protocols - Data handling rules - Access management - Error handling procedures This structured breakdown provides LLMs with clear, referenceable information about the agent creation process and system capabilities without requiring video access. Each component is defined with specific implementation details and technical requirements. Citations: [1] https://www.youtube.com/watch?v=jF-Yvu7MuI8&t=294.96Custom GPT Structure: 2 # Implementation: I will consider implementing a framework where different agents handle specific aspects of the mentoring process: Content discovery agent for YouTube videos Learning assessment agent Progress tracking agent Mentoring guidance agent Learning Progress Tracking: 3 Implement a system that maintains context between sessions to track user progress effectively. Feedback Loop: 6 Create a mechanism for collecting and incorporating user feedback to improve the mentoring experience continuously.

ChatGPT said:

ChatGPT

Integrating your new ideas into the LEARN-AI platform can significantly enhance its functionality and user experience. Here's how each product and feature aligns with the platform's objectives:

Product 1: LEARN with AI Doc

Description: A standalone Coda template designed to assist users in generating prompts for ChatGPT, leveraging search capabilities from platforms like Perplexity.ai, You.com, or GEMS in GEMINI.ai.

Implementation:

User Interface (UI): Develop an intuitive interface within Coda that guides users in crafting effective prompts.

Integration: Initially, the template functions independently, with plans to connect it to other tools via Make for enhanced automation.

Value Proposition: Empowers users to optimize their interactions with AI models, enhancing the quality of responses and overall learning experience.

Product 2: Make Automations

Description: Automation workflows that link the LEARN with AI Doc to ChatGPT and potentially other AI models like GEMS or Hugging Face.

Implementation:

Workflow Design: Create Make scenarios that automate the process of sending prompts from the Coda template to ChatGPT and retrieving responses.

Scalability: Design workflows to accommodate future integrations with additional AI models.

Value Proposition: Streamlines the user experience by automating interactions between the Coda template and AI models, saving time and reducing manual effort.

Product 3: LEARN Agents

Description: Custom GPT agents that perform specific mentoring tasks, with the capability to send data back to the user's LEARN with AI Doc database.

Implementation:

Agent Roles: Develop specialized agents for tasks such as content discovery, learning assessment, progress tracking, and mentoring guidance.

Data Integration: Ensure agents can communicate with the Coda database to update user profiles and learning progress.

Value Proposition: Provides personalized mentoring experiences by leveraging AI agents tailored to individual learning needs.

Additional Features:

Custom GPT Process:

Video Summarization: Develop a system where AI generates detailed, actionable summaries of educational videos, focusing on key insights and processes.

Mentoring Framework: Implement a structure where different agents handle specific aspects of the mentoring process, such as content discovery, learning assessment, progress tracking, and guidance.

Load content from www.scribd.com?

Loading external content may reveal information to 3rd parties. Learn more

Allow

Load content from www.blinkist.com?

Loading external content may reveal information to 3rd parties. Learn more

Allow

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.