I’m just getting started with it for no-code (actually having install issues). Previously used bubble, gptengineer, wordpress (a lot) and others. Been building for the web for 30 years. I would say try it on bolt.new website first, then come here and learn to install it locally if you like it. Also try gptengineer. Its very good and gets you a long with simple apps.
This would be fun podcast with a 4-man podcast to demo ideas to show and tell discussing how to learn how to build context to create apps. Easier said then done. Learn development buzzwords for Frontend and backend development. Hey AI teach me how to learn: “Learn development buzzwords for Frontend and backend development” or Start by asking ChatGPT: “Hey, build out a structured learning tasks on how to learn how to build context inception mode with the goal to become a no-code AI developer” without knowing Python or any programming language."
Response:
Structured Learning Plan to Become a No-Code AI Developer Focusing on Context Inception Mode
Introduction
Embarking on a journey to become a no-code AI developer is an exciting venture. This structured learning plan is designed to guide you step-by-step toward building AI applications with advanced context management—what we’ll refer to as “Context Inception Mode.” This mode involves creating AI systems that can understand, manage, and utilize context in a sophisticated manner, enhancing user interactions without the need for traditional coding languages like Python.
Module 1: Foundations of AI and No-Code Development
Objective: Understand the basics of Artificial Intelligence (AI), Machine Learning (ML), and No-Code development platforms.
Task 1.1: Learn the Basics of AI and ML
Action Steps:
Read Introductory Articles:
“Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig (Overview).
Online articles explaining AI and ML concepts.
Watch Educational Videos:
Khan Academy’s introductory videos on AI.
Coursera’s free courses on AI fundamentals.
Task 1.2: Understand No-Code Development
Action Steps:
Research No-Code Platforms:
Explore what No-Code platforms are and how they work.
Read about the benefits and limitations of No-Code development.
Explore Use Cases:
Look into real-world applications built using No-Code tools.
Module 2: Introduction to Large Language Models and Context
Objective: Grasp how Large Language Models (LLMs) like GPT-4 process language and utilize context.
Task 2.1: Learn About LLMs
Action Steps:
Study LLM Architectures:
Understand transformers and how they enable language processing.
Read About GPT Models:
Explore OpenAI’s documentation on GPT-3 and GPT-4.
Task 2.2: Understand Context in AI Models
Action Steps:
Learn About Context Windows:
Understand how AI models use context windows to generate responses.
Explore Context Limitations:
Study the limitations of context length and strategies to manage it.
Resources:
Articles:
“Attention Is All You Need” (Vaswani et al., 2017).
Documentation:
OpenAI’s GPT-4 technical report.
Module 3: Exploring No-Code AI Tools
Objective: Familiarize yourself with No-Code AI platforms that allow you to build applications without programming knowledge.
Task 3.1: Identify Popular No-Code AI Platforms
Action Steps:
Research Platforms:
Bubble, Zapier, Make (formerly Integromat), Airtable, Adalo, and Voiceflow.
Compare Features:
Assess each platform’s capabilities, integrations, and pricing models.
Task 3.2: Get Hands-On Experience
Action Steps:
Sign Up and Explore:
Create accounts on selected platforms.
Follow Tutorials:
Complete beginner tutorials to understand the basics of each tool.
Resources:
Platforms:
[Bubble io]bubble io)
[Zapier com](zapier com)
Tutorials:
Official getting-started guides on each platform.
Module 4: Understanding and Managing Context in AI Applications
Objective: Learn how to handle and utilize context within AI applications built on No-Code platforms.
Task 4.1: Study Context Management Techniques
Action Steps:
Learn About State Management:
Understand how to store and retrieve user inputs.
Explore Conversation Design:
Study how context affects user experience in chatbots.
Task 4.2: Implement Context Handling
Action Steps:
Build a Context-Aware Chatbot:
Use a No-Code platform to create a chatbot that remembers previous interactions.
Utilize Variables and Data Storage:
Learn how to use platform features to maintain conversation context.
Resources:
Articles:
“Designing Conversational Chatbots with Context” on Chatbots Magazine.
Tutorials:
Platform-specific guides on context management.
Module 5: Introduction to Prompt Engineering
Objective: Master crafting effective prompts to guide AI models toward desired outputs.
Task 5.1: Learn Prompt Engineering Principles
Action Steps:
Study Prompt Structures:
Understand how different phrasings affect AI responses.
Explore Techniques:
Few-shot learning, zero-shot learning, and role prompting.
Task 5.2: Practice Crafting Prompts
Action Steps:
Experiment with Prompts:
Use AI playgrounds to see how changes in prompts alter outputs.
Analyze Responses:
Note patterns in AI behavior based on your prompts.
Resources:
Guides:
OpenAI’s “Best Practices for Prompt Engineering.”
Tools:
OpenAI Playground or AI Dungeon.
Module 6: Conceptualizing Context Inception Mode
Objective: Develop an understanding of advanced context utilization techniques in AI applications.
Task 6.1: Define Context Inception Mode
Action Steps:
Theorize the Concept:
Consider how context inception could involve AI creating and managing its own context layers.
Relate to Existing Techniques:
Compare with recursive prompting and meta-learning.
Task 6.2: Explore Advanced Context Techniques
Action Steps:
Study Meta-Prompting:
Learn how to create prompts that instruct the AI to consider previous interactions creatively.
Implement Recursive Prompts:
Practice prompts that reference themselves or previous outputs.
Resources:
Articles:
“Advanced Prompt Engineering Techniques” on Towards Data Science.
Research Papers:
Papers on meta-learning and recursive neural networks.
Module 7: Building Advanced Context-Aware AI Applications
Objective: Apply your knowledge to create AI applications utilizing advanced context management.
Task 7.1: Design Your AI Application
Action Steps:
Define Objectives:
Decide what problem your application will solve.
Plan Features:
Outline how context inception will enhance functionality.
Task 7.2: Develop the Application
Action Steps:
Use No-Code Tools:
Build your application on a chosen platform.
Integrate Advanced Prompts:
Apply your prompt engineering skills for context management.
Task 7.3: Test and Refine
Action Steps:
User Testing:
Gather feedback from real users.
Iterate:
Make improvements based on feedback.
Resources:
Templates:
Project planning templates from platforms like Trello or Asana.
Feedback Tools:
Surveys and user testing platforms.
Module 8: Deployment and Scaling
Objective: Learn how to deploy your AI application and plan for growth.
Task 8.1: Deploy Your Application
Action Steps:
Follow Deployment Procedures:
Use your platform’s guidelines to make your application live.
Legal Compliance:
Ensure your application complies with data protection laws.
Task 8.2: Plan for Scalability
Action Steps:
Understand Scalability Options:
Learn how to scale resources as user demand grows.
Implement Monitoring:
Set up tools to monitor performance and user engagement.
Resources:
Platform Documentation:
Deployment and scaling guides.
Monitoring Tools:
Analytics features within your No-Code platform.
Module 9: Ethics and Responsible AI
Objective: Incorporate ethical considerations into your AI application.
Task 9.1: Study AI Ethics
Action Steps:
Understand Bias and Fairness:
Learn how AI can inadvertently perpetuate biases.
Privacy Concerns:
Study how to handle user data responsibly.
Task 9.2: Implement Ethical Practices
Action Steps:
Bias Mitigation:
Implement strategies to reduce bias in your AI’s outputs.
Transparent Communication:
Inform users about how their data is used.
Resources:
Guidelines:
“Ethics Guidelines for Trustworthy AI” by the European Commission.
Articles:
“The Ethical Implications of AI” on MIT Technology Review.
Module 10: Continuous Learning and Community Engagement
Objective: Stay updated with AI advancements and engage with the community.
Task 10.1: Join Communities
Action Steps:
Participate in Forums:
Engage in discussions on platforms like Reddit, Stack Overflow, and LinkedIn groups.
Attend Webinars and Workshops:
Join events hosted by AI and No-Code communities.
Task 10.2: Expand Your Skills
Action Steps:
Take Advanced Courses:
Enroll in courses that delve deeper into AI and No-Code development.
Experiment with New Tools:
Stay abreast of emerging platforms and technologies.
Resources:
Communities:
[NoCodeDevs](nocodedevs com)
Courses:
Curated Youtube Playlist.
Conclusion
By diligently following this structured learning plan, you will acquire the skills and knowledge to become a proficient no-code AI developer. You’ll be capable of building sophisticated AI applications that utilize advanced context management techniques—bringing your vision of “Context Inception Mode” to life—all without the need to learn traditional programming languages.
Next Steps:
Set a Timeline: Allocate time each week to work through the modules.
Stay Committed: Regular practice and application of what you’ve learned are key.
Seek Feedback: Don’t hesitate to ask questions in communities or seek mentorship.
Remember: The field of AI is constantly evolving. Continuous learning and adaptation are essential. Good luck on your journey to becoming a no-code AI developer!