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📚 Resources

Resources

🎯 Overview​

This page contains curated resources to help you master AI-driven development, including tools, guides, and learning materials from the Panaversity community.

🤖 AI Tools​

Language Models & APIs​

OpenAI​

  • GPT-4 & GPT-3.5: Industry-leading language models
  • DALL-E: AI image generation
  • Whisper: Speech-to-text
  • Text Embeddings: Semantic search capabilities
  • 🔗 API Documentation
  • 🔗 Playground

Anthropic Claude​

  • Claude 3.5 Sonnet: Advanced reasoning and coding
  • Claude 3 Opus: Highest intelligence
  • Claude 3 Haiku: Fast and efficient
  • 🔗 API Documentation
  • 🔗 Console

Google Gemini​

Meta LLaMA​

  • LLaMA 2: Open-source foundation models
  • Code LLaMA: Specialized for coding
  • 🔗 Download
  • 🔗 GitHub

Development Tools​

GitHub Copilot​

  • AI-powered code completion
  • Multi-language support
  • IDE integration
  • 🔗 Website

Cursor AI​

  • AI-first code editor
  • Built-in Claude integration
  • Codebase awareness
  • 🔗 Website

Claude Code​

  • Terminal-based AI assistant
  • Full codebase context
  • Multi-file editing
  • 🔗 Documentation

v0.dev​

  • AI UI/UX generation
  • React/Next.js components
  • Real-time preview
  • 🔗 Website

Replit AI​

  • Cloud IDE with AI
  • Instant deployment
  • Collaborative coding
  • 🔗 Website

Vector Databases​

Qdrant​

Pinecone​

Weaviate​

  • GraphQL interface
  • Hybrid search
  • Multi-tenancy
  • 🔗 Documentation

ChromaDB​

AI Frameworks​

LangChain​

LangGraph​

  • Multi-agent orchestration
  • Stateful workflows
  • Built on LangChain
  • 🔗 Documentation

LlamaIndex​

AutoGen​

💡 Prompt Engineering​

Panaversity Resources​

The Panaversity GitHub organization contains extensive learning materials for AI-driven development:

Key Repositories​

🔗 Learn Agentic AI​

Comprehensive guide to building AI agents:

  • Agent architectures
  • Tool use and function calling
  • Multi-agent systems
  • Production deployment
🔗 Learn Generative AI​

Foundation of GenAI development:

  • LLM fundamentals
  • Fine-tuning techniques
  • RAG implementations
  • Prompt engineering
🔗 Learn Prompt Engineering​

Master the art of prompting:

  • Basic to advanced techniques
  • Chain-of-thought prompting
  • Few-shot learning
  • Prompt optimization

Essential Prompt Patterns​

1. Zero-Shot Prompting​

Task: [Describe what you want]
Format: [Specify output format]
Constraints: [Define limitations]

Example:

Task: Summarize this article in 3 bullet points
Format: Markdown list
Constraints: Each point must be under 20 words

2. Few-Shot Learning​

Here are examples of the desired output:

Example 1:
Input: [example input 1]
Output: [example output 1]

Example 2:
Input: [example input 2]
Output: [example output 2]

Now do the same for:
Input: [your actual input]

3. Chain-of-Thought (CoT)​

Let's solve this step by step:

1. First, [step 1]
2. Then, [step 2]
3. Next, [step 3]
4. Finally, [step 4]

Therefore, the answer is [conclusion]

4. Role-Based Prompting​

You are a [role] with expertise in [domain].
Your task is to [specific task].
Consider [important factors].
Provide [type of output].

Example:

You are a senior software architect with expertise in microservices.
Your task is to review this API design.
Consider scalability, security, and maintainability.
Provide specific recommendations with examples.

5. Structured Output​

Generate a [type] with the following structure:

{
  "field1": "description",
  "field2": "description",
  "field3": ["list", "of", "items"]
}

Requirements:
- [requirement 1]
- [requirement 2]

6. Iterative Refinement​

Version 1: [initial attempt]

Issues with Version 1:
- [issue 1]
- [issue 2]

Version 2 (improved): [refined version]

Addressing: [how issues were fixed]

Prompt Engineering Best Practices​

Be Specific​

❌ Bad: "Write code for authentication" ✅ Good: "Write a TypeScript function for JWT-based authentication with email/password, including password hashing with bcrypt and token expiration"

Provide Context​

❌ Bad: "Fix this bug" ✅ Good: "This React component crashes when the API returns null. The error occurs in the useEffect hook. Here's the code: [code]. Expected behavior: gracefully handle null responses"

Use Delimiters​

Analyze the following code:
'''
[your code here]
'''

Focus on:
1. Performance issues
2. Security vulnerabilities
3. Best practice violations

Specify Output Format​

Generate a REST API specification in OpenAPI 3.0 format with:
- Authentication endpoints
- CRUD operations for users
- Proper error codes
- Request/response examples

Request Explanations​

Implement a binary search algorithm in Python.

Then explain:
1. Time complexity
2. Space complexity
3. When to use vs linear search
4. Edge cases handled

Advanced Techniques​

Meta-Prompting​

Ask the AI to help you write better prompts:

I want to write a prompt that generates high-quality REST API documentation.

Suggest an optimal prompt structure that includes:
- All necessary context
- Clear output format
- Quality criteria
- Examples

Then use that structure to generate documentation for a task management API.

Prompt Chaining​

Break complex tasks into steps:

Step 1: Analyze these user requirements and extract key features
Step 2: For each feature, define acceptance criteria
Step 3: Create API endpoints for each feature
Step 4: Generate test cases for the endpoints

Self-Critique​

Ask the AI to review its own output:

Generate a Python function for email validation.

Then critique your own code:
- What edge cases are missed?
- How can performance be improved?
- What security issues exist?

Finally, provide an improved version addressing the issues.

📖 Learning Resources​

Official Documentation​

Panaversity Learning Paths​

Visit Panaversity GitHub for:

  • 🎓 Structured learning curriculum
  • 💻 Hands-on projects
  • 🛠️ Code examples and templates
  • 📚 Comprehensive guides
  • 🤝 Community support

Generative AI Fundamentals​

  • LLM architecture and training
  • Tokenization and embeddings
  • Fine-tuning techniques
  • Prompt engineering basics

Agentic AI Development​

  • Agent design patterns
  • Tool use and function calling
  • Multi-agent orchestration
  • Memory and state management

RAG Systems​

  • Vector databases
  • Embedding strategies
  • Retrieval optimization
  • Hybrid search techniques

Production Deployment​

  • API design and scaling
  • Cost optimization
  • Monitoring and observability
  • Security best practices

🔧 Development Tools​

IDEs & Editors​

Version Control​

Containerization​

API Development​

🌐 Community & Support​

Panaversity Community​

  • GitHub: https://github.com/panaversity
  • Discord: Join the community for discussions
  • YouTube: Video tutorials and walkthroughs
  • Blog: Latest updates and articles

Other Communities​

📝 Additional Resources​

Blogs & Articles​

Research Papers​

Datasets​

🚀 Getting Started​

  1. Choose your learning path from Panaversity repositories
  2. Set up your development environment with the tools above
  3. Join the community for support and collaboration
  4. Build projects to practice and learn
  5. Share your progress and help others

Ready to dive deeper? Head over to Panaversity GitHub to access all learning materials and start your AI-driven development journey! 🎓

👨‍💻 Developer​

This book and website were developed by Ibrahim Samad

Follow me for more AI-driven development projects, tools, and resources!