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+# Core Generative AI Techniques - Project Documentation
+
+This document provides comprehensive documentation for the first half of the projects in the Core Generative AI Techniques lesson. Each project demonstrates different AI capabilities, frameworks, and integration patterns.
+
+## Quick Navigation
+
+- [01 Basic Chat Projects](#01-basic-chat-projects)
+- [02 Functions Projects](#02-functions-projects)
+- [03 RAG (Retrieval-Augmented Generation) Projects](#03-rag-retrieval-augmented-generation-projects)
+- [04 Vision Analysis Projects](#04-vision-analysis-projects)
+- [05 Audio Projects](#05-audio-projects)
+- [06 Agents Projects](#06-agents-projects)
+
+---
+
+## 01 Basic Chat Projects
+
+### BasicChat-01MEAI
+**Short Description**: Basic chat implementation using Microsoft Extensions AI with GitHub Models API
+
+**Dependencies**:
+- `Microsoft.Extensions.AI` (9.6.0) - Core AI abstractions
+- `Microsoft.Extensions.AI.AzureAIInference` (9.6.0-preview.1.25310.2) - Azure AI inference integration
+- `Microsoft.Extensions.Configuration.UserSecrets` (9.0.6) - Configuration management
+
+**Main Features**:
+- Sentiment analysis of product reviews
+- Uses GitHub Models API with Phi-3.5-MoE-instruct model
+- Demonstrates basic prompt construction and response handling
+- Environment variable and user secrets configuration
+
+**How to Run/Demo**:
+1. Set up GitHub token in user secrets: `dotnet user-secrets set "GITHUB_TOKEN" "your-token"`
+2. Run: `dotnet run`
+3. The app analyzes predefined product reviews and outputs sentiment analysis
+
+**AI Techniques**: Text completion, sentiment analysis, prompt engineering
+**Tags**: #MEAI #GitHubModels #Chat #SentimentAnalysis #BasicAI
+
+### BasicChat-02SK
+**Short Description**: Chat implementation using Semantic Kernel framework with OpenAI integration
+
+**Dependencies**:
+- `Microsoft.SemanticKernel` (1.55.0) - Semantic Kernel framework
+- `Microsoft.Extensions.Configuration.UserSecrets` (9.0.2) - Configuration management
+
+**Main Features**:
+- Interactive chat using Semantic Kernel
+- OpenAI client integration with GitHub Models
+- Chat history management
+- Uses Phi-3.5-mini-instruct model
+
+**How to Run/Demo**:
+1. Configure GitHub token in user secrets
+2. Run: `dotnet run`
+3. Interactive chat session with AI model
+
+**AI Techniques**: Chat completion, conversation management, Semantic Kernel patterns
+**Tags**: #SemanticKernel #OpenAI #GitHubModels #Chat #Interactive
+
+### BasicChat-03Ollama
+**Short Description**: Local AI chat using Ollama with Microsoft Extensions AI
+
+**Dependencies**:
+- `Microsoft.Extensions.AI` (9.6.0) - Core AI abstractions
+- Ollama local server integration
+
+**Main Features**:
+- Local AI model execution via Ollama
+- Sentiment analysis of product reviews
+- No cloud dependencies required
+- Uses phi4-mini model locally
+
+**How to Run/Demo**:
+1. Install and start Ollama: `ollama serve`
+2. Pull model: `ollama pull phi4-mini`
+3. Run: `dotnet run`
+4. Analyzes reviews using local AI model
+
+**AI Techniques**: Local AI inference, sentiment analysis, offline processing
+**Tags**: #Ollama #LocalAI #MEAI #SentimentAnalysis #OfflineAI
+
+### BasicChat-04OllamaSK
+**Short Description**: Semantic Kernel integration with Ollama for local AI chat
+
+**Dependencies**:
+- `Microsoft.SemanticKernel` (1.54.0) - Semantic Kernel framework
+- `Microsoft.SemanticKernel.Connectors.Ollama` (1.54.0-alpha) - Ollama connector
+- `Microsoft.Extensions.Configuration.UserSecrets` (9.0.5) - Configuration
+
+**Main Features**:
+- Combines Semantic Kernel with local Ollama models
+- Interactive chat with local AI
+- No cloud API keys required
+- Demonstrates local-first AI development
+
+**How to Run/Demo**:
+1. Start Ollama server
+2. Pull required model (check Program.cs for model name)
+3. Run: `dotnet run`
+4. Interactive chat with local AI model
+
+**AI Techniques**: Local AI inference, Semantic Kernel patterns, chat completion
+**Tags**: #SemanticKernel #Ollama #LocalAI #Chat #Interactive
+
+---
+
+## 02 Functions Projects
+
+### SKFunctions01
+**Short Description**: Semantic Kernel with custom function plugins for weather data
+
+**Dependencies**:
+- `Microsoft.SemanticKernel` (1.55.0) - Core framework
+- `Microsoft.SemanticKernel.Connectors.Ollama` (1.54.0-alpha) - Ollama support
+- `Microsoft.Extensions.Configuration.UserSecrets` (9.0.5) - Configuration
+
+**Main Features**:
+- Custom weather plugin (`CityTemperaturePlugIn`)
+- Function calling with AI models
+- Interactive chat with tool access
+- Supports both cloud and local models
+
+**How to Run/Demo**:
+1. Configure API credentials
+2. Run: `dotnet run`
+3. Ask weather-related questions: "What's the temperature in London?"
+4. AI will use the custom function to provide weather data
+
+**AI Techniques**: Function calling, plugin architecture, tool use
+**Tags**: #SemanticKernel #Functions #Plugins #Weather #ToolUse
+
+### MEAIFunctions
+**Short Description**: Microsoft Extensions AI with function calling capabilities
+
+**Dependencies**:
+- `Microsoft.Extensions.AI` (9.6.0) - Core AI framework
+- Function calling implementations
+
+**Main Features**:
+- Demonstrates MEAI function calling patterns
+- Tool integration with AI models
+- Custom function definitions
+- Response handling with tool results
+
+**How to Run/Demo**:
+1. Set up API credentials
+2. Run: `dotnet run`
+3. Interact with AI using function-enabled prompts
+
+**AI Techniques**: Function calling, tool integration, MEAI patterns
+**Tags**: #MEAI #Functions #ToolCalling #FunctionIntegration
+
+### MEAIFunctionsOllama
+**Short Description**: Local function calling using Ollama with MEAI
+
+**Dependencies**:
+- `Microsoft.Extensions.AI` framework
+- Ollama integration for local models
+- Custom function implementations
+
+**Main Features**:
+- Local function calling without cloud dependencies
+- Ollama model integration
+- Tool use with local AI models
+- Demonstrates offline AI capabilities
+
+**How to Run/Demo**:
+1. Start Ollama server
+2. Ensure compatible model is available
+3. Run: `dotnet run`
+4. Test function calling with local AI
+
+**AI Techniques**: Local function calling, offline AI, tool use
+**Tags**: #MEAI #Ollama #LocalFunctions #ToolUse #OfflineAI
+
+### MEAIFunctionsAzureOpenAI
+**Short Description**: Azure OpenAI integration with MEAI function calling
+
+**Dependencies**:
+- `Microsoft.Extensions.AI` framework
+- Azure OpenAI connectors
+- Function calling capabilities
+
+**Main Features**:
+- Azure OpenAI service integration
+- Production-ready function calling
+- Enterprise AI scenarios
+- Scalable AI function architecture
+
+**How to Run/Demo**:
+1. Configure Azure OpenAI credentials
+2. Set up required environment variables
+3. Run: `dotnet run`
+4. Test function calling with Azure models
+
+**AI Techniques**: Azure AI services, function calling, enterprise AI
+**Tags**: #MEAI #AzureOpenAI #Functions #Enterprise #CloudAI
+
+---
+
+## 03 RAG (Retrieval-Augmented Generation) Projects
+
+### RAGSimple-02MEAIVectorsMemory
+**Short Description**: In-memory vector storage for RAG using MEAI
+
+**Dependencies**:
+- `Microsoft.Extensions.AI.Ollama` (9.5.0-preview.1.25265.7) - Ollama integration
+- `Microsoft.Extensions.VectorData.Abstractions` (9.5.0) - Vector data abstractions
+- `Microsoft.SemanticKernel.Connectors.InMemory` (1.54.0-preview) - In-memory vector store
+- `MEAIVectorsShared` - Shared utilities
+
+**Main Features**:
+- In-memory vector database
+- Document embedding and retrieval
+- RAG pipeline implementation
+- Local vector storage without external dependencies
+
+**How to Run/Demo**:
+1. Start Ollama with embedding model
+2. Run: `dotnet run`
+3. Add documents and query for relevant information
+4. See RAG responses based on stored documents
+
+**AI Techniques**: Vector embeddings, semantic search, RAG, in-memory storage
+**Tags**: #RAG #VectorDB #Embeddings #MEAI #InMemory
+
+### MEAIVectorsShared
+**Short Description**: Shared utilities and models for vector operations
+
+**Dependencies**:
+- Common vector data models
+- Utility functions for RAG projects
+
+**Main Features**:
+- Shared data models for vector operations
+- Common utilities for embedding and retrieval
+- Reusable components across RAG projects
+- Abstraction layer for vector operations
+
+**How to Run/Demo**:
+This is a shared library referenced by other RAG projects.
+
+**AI Techniques**: Vector data modeling, shared abstractions
+**Tags**: #Shared #VectorData #Utilities #RAG
+
+### RAGSimple-03MEAIVectorsAISearch
+**Short Description**: Azure AI Search integration for production RAG scenarios
+
+**Dependencies**:
+- Azure AI Search connectors
+- MEAI framework integration
+- Vector search capabilities
+
+**Main Features**:
+- Azure AI Search integration
+- Production-scale vector search
+- Hybrid search capabilities (text + vector)
+- Enterprise RAG implementation
+
+**How to Run/Demo**:
+1. Set up Azure AI Search service
+2. Configure search credentials
+3. Run: `dotnet run`
+4. Index documents and perform RAG queries
+
+**AI Techniques**: Azure AI Search, hybrid search, production RAG
+**Tags**: #RAG #AzureAISearch #Production #HybridSearch #Enterprise
+
+### RAGSimple-04MEAIVectorsQdrant
+**Short Description**: Qdrant vector database integration for RAG
+
+**Dependencies**:
+- Qdrant vector database connectors
+- MEAI integration
+- Vector storage and retrieval
+
+**Main Features**:
+- Qdrant vector database integration
+- High-performance vector search
+- Persistent vector storage
+- Scalable RAG implementation
+
+**How to Run/Demo**:
+1. Start Qdrant server (Docker recommended)
+2. Configure Qdrant connection
+3. Run: `dotnet run`
+4. Store and retrieve documents using vector similarity
+
+**AI Techniques**: Qdrant vector DB, persistent storage, scalable RAG
+**Tags**: #RAG #Qdrant #VectorDB #Persistent #Scalable
+
+### RAGSimple-10SKOllama
+**Short Description**: Semantic Kernel RAG implementation with local Ollama models
+
+**Dependencies**:
+- `Microsoft.SemanticKernel` framework
+- Ollama integration
+- Vector memory connectors
+
+**Main Features**:
+- Local RAG with Semantic Kernel
+- Ollama model integration
+- Memory-based document storage
+- Offline RAG capabilities
+
+**How to Run/Demo**:
+1. Start Ollama with text and embedding models
+2. Run: `dotnet run`
+3. Add documents to memory
+4. Query for information with RAG responses
+
+**AI Techniques**: Local RAG, Semantic Kernel memory, offline processing
+**Tags**: #SemanticKernel #RAG #Ollama #LocalRAG #Memory
+
+### RAGSimple-15Ollama-DeepSeekR1
+**Short Description**: RAG implementation using DeepSeek R1 model via Ollama
+
+**Dependencies**:
+- Ollama integration
+- DeepSeek R1 model support
+- RAG pipeline components
+
+**Main Features**:
+- Advanced reasoning with DeepSeek R1
+- Local RAG with reasoning capabilities
+- Enhanced document understanding
+- Advanced query processing
+
+**How to Run/Demo**:
+1. Pull DeepSeek R1 model: `ollama pull deepseek-r1`
+2. Run: `dotnet run`
+3. Test RAG with complex reasoning queries
+
+**AI Techniques**: Advanced reasoning, local RAG, DeepSeek models
+**Tags**: #RAG #DeepSeek #Reasoning #Ollama #AdvancedAI
+
+---
+
+## 04 Vision Analysis Projects
+
+### Vision-01MEAI-GitHubModels
+**Short Description**: Vision AI using GitHub Models API with Microsoft Extensions AI
+
+**Dependencies**:
+- `Microsoft.Extensions.AI` (9.5.0) - Core AI framework
+- `Microsoft.Extensions.AI.AzureAIInference` (9.5.0-preview.1.25265.7) - Azure inference
+- `OpenCvSharp4.runtime.win` (4.10.0.20241108) - Image processing
+- `Microsoft.Extensions.Configuration.UserSecrets` (9.0.5) - Configuration
+
+**Main Features**:
+- Image analysis using GitHub Models
+- Multiple image formats support
+- OCR and image understanding
+- Sample images included (receipt, license, shoes)
+
+**How to Run/Demo**:
+1. Configure GitHub token
+2. Run: `dotnet run`
+3. Analyzes included sample images
+4. Extracts text and provides image descriptions
+
+**AI Techniques**: Computer vision, OCR, image analysis, multimodal AI
+**Tags**: #Vision #MEAI #GitHubModels #OCR #ImageAnalysis
+
+### Vision-02MEAI-Ollama
+**Short Description**: Local vision AI using Ollama with multimodal models
+
+**Dependencies**:
+- MEAI framework
+- Ollama multimodal model support
+- Local image processing
+
+**Main Features**:
+- Local vision model processing
+- Multimodal AI capabilities
+- No cloud dependencies
+- Image understanding and description
+
+**How to Run/Demo**:
+1. Pull multimodal model: `ollama pull llava` or similar
+2. Run: `dotnet run`
+3. Process images locally
+4. Get image descriptions and analysis
+
+**AI Techniques**: Local vision AI, multimodal models, offline processing
+**Tags**: #Vision #Ollama #LocalAI #Multimodal #OfflineVision
+
+### Vision-03MEAI-AOAI
+**Short Description**: Azure OpenAI vision capabilities with MEAI
+
+**Dependencies**:
+- MEAI framework
+- Azure OpenAI connectors
+- Vision model integration
+
+**Main Features**:
+- Azure OpenAI Vision integration
+- GPT-4V or similar model usage
+- Production vision AI
+- Enterprise-grade image analysis
+
+**How to Run/Demo**:
+1. Configure Azure OpenAI credentials
+2. Run: `dotnet run`
+3. Upload or process images
+4. Get detailed image analysis
+
+**AI Techniques**: Azure OpenAI Vision, GPT-4V, enterprise vision AI
+**Tags**: #Vision #AzureOpenAI #MEAI #GPT4V #Enterprise
+
+### Vision-04MEAI-AOAI-Spectre
+**Short Description**: Advanced vision processing with Spectre.Console UI
+
+**Dependencies**:
+- MEAI framework
+- Azure OpenAI integration
+- Spectre.Console for enhanced UI
+- Advanced image processing
+
+**Main Features**:
+- Rich console UI with Spectre.Console
+- Advanced image analysis workflows
+- Interactive vision AI application
+- Enhanced user experience
+
+**How to Run/Demo**:
+1. Set up Azure OpenAI credentials
+2. Run: `dotnet run`
+3. Use interactive menu for image processing
+4. Enhanced visual feedback and results
+
+**AI Techniques**: Vision AI, interactive UI, enhanced UX
+**Tags**: #Vision #AzureOpenAI #SpectreConsole #Interactive #UX
+
+---
+
+## 05 Audio Projects
+
+### Audio-01-SpeechMic
+**Short Description**: Speech recognition and audio processing from microphone input
+
+**Dependencies**:
+- Audio processing libraries
+- Speech recognition services
+- Microphone access components
+
+**Main Features**:
+- Real-time microphone input
+- Speech-to-text conversion
+- Audio preprocessing
+- Voice command recognition
+
+**How to Run/Demo**:
+1. Ensure microphone permissions
+2. Run: `dotnet run`
+3. Speak into microphone
+4. See real-time speech transcription
+
+**AI Techniques**: Speech recognition, audio processing, real-time AI
+**Tags**: #Audio #SpeechToText #Microphone #RealTime #Voice
+
+### Audio-02-RealTimeAudio
+**Short Description**: Real-time audio processing and AI integration
+
+**Dependencies**:
+- Real-time audio libraries
+- AI audio processing
+- Streaming audio components
+
+**Main Features**:
+- Real-time audio stream processing
+- Low-latency audio AI
+- Continuous audio analysis
+- Streaming audio capabilities
+
+**How to Run/Demo**:
+1. Configure audio devices
+2. Run: `dotnet run`
+3. Process audio in real-time
+4. See continuous AI analysis
+
+**AI Techniques**: Real-time audio AI, stream processing, low-latency inference
+**Tags**: #Audio #RealTime #Streaming #LowLatency #AudioAI
+
+---
+
+## 06 Agents Projects
+
+### AgentLabs-01-Simple
+**Short Description**: Basic AI agent using Azure AI Projects SDK
+
+**Dependencies**:
+- `Azure.AI.Agents.Persistent` (1.1.0-beta.1) - Agent persistence
+- `Azure.AI.Projects` (1.0.0-beta.9) - Azure AI Projects
+- `Azure.Identity` (1.14.0) - Authentication
+- `Microsoft.Extensions.Configuration.UserSecrets` (9.0.5) - Configuration
+
+**Main Features**:
+- Simple AI agent implementation
+- Azure AI Projects integration
+- Agent state management
+- Basic agent workflows
+
+**How to Run/Demo**:
+1. Set up Azure AI Projects credentials
+2. Run: `dotnet run`
+3. Interact with the AI agent
+4. See agent responses and state management
+
+**AI Techniques**: AI agents, state management, agent orchestration
+**Tags**: #Agents #AzureAI #AgentOrchestration #StateManagement
+
+### AgentLabs-02-Functions
+**Short Description**: AI agents with custom function capabilities
+
+**Dependencies**:
+- Azure AI Projects SDK
+- Custom function implementations
+- Agent function calling
+
+**Main Features**:
+- Agents with tool access
+- Custom function definitions
+- Agent-tool integration
+- Complex agent workflows
+
+**How to Run/Demo**:
+1. Configure agent and function credentials
+2. Run: `dotnet run`
+3. Ask agent to perform tasks requiring tools
+4. See agent function calling in action
+
+**AI Techniques**: Agent function calling, tool integration, complex workflows
+**Tags**: #Agents #Functions #ToolUse #AgentWorkflows
+
+### AgentLabs-03-OpenAPIs
+**Short Description**: Agents with OpenAPI integration for external service access
+
+**Dependencies**:
+- Azure AI Projects SDK
+- OpenAPI client generation
+- External API integration
+
+**Main Features**:
+- OpenAPI specification integration
+- External service access
+- Agent API orchestration
+- Complex multi-service workflows
+
+**How to Run/Demo**:
+1. Set up API credentials and endpoints
+2. Run: `dotnet run`
+3. Request agent to use external APIs
+4. See multi-service agent orchestration
+
+**AI Techniques**: Agent API integration, OpenAPI, service orchestration
+**Tags**: #Agents #OpenAPI #APIIntegration #ServiceOrchestration
+
+---
+
+## Getting Started
+
+### Prerequisites
+- .NET 9.0 SDK
+- Visual Studio 2022 or VS Code
+- Appropriate API keys/tokens for cloud services
+- Ollama installed for local projects
+
+### Configuration
+Most projects use user secrets for configuration. Set up credentials using:
+```bash
+dotnet user-secrets set "GITHUB_TOKEN" "your-token"
+dotnet user-secrets set "AZURE_OPENAI_ENDPOINT" "your-endpoint"
+dotnet user-secrets set "AZURE_OPENAI_APIKEY" "your-key"
+```
+
+### Running Projects
+1. Navigate to project directory
+2. Install dependencies: `dotnet restore`
+3. Run project: `dotnet run`
+
+### Common Issues
+- **API Keys**: Ensure all required API keys are configured
+- **Ollama**: For local projects, verify Ollama is running and models are pulled
+- **Dependencies**: Some projects require specific versions - check .csproj files
+
+---
+
+## Additional Resources
+- [Semantic Kernel Documentation](https://learn.microsoft.com/semantic-kernel/)
+- [Microsoft Extensions AI](https://learn.microsoft.com/dotnet/ai/)
+- [Azure OpenAI Service](https://azure.microsoft.com/services/openai-service/)
+- [Ollama Documentation](https://ollama.com/docs)
\ No newline at end of file
diff --git a/03-CoreGenerativeAITechniques/readme.md b/03-CoreGenerativeAITechniques/readme.md
index abf460d..0800d79 100644
--- a/03-CoreGenerativeAITechniques/readme.md
+++ b/03-CoreGenerativeAITechniques/readme.md
@@ -29,3 +29,18 @@ For this lesson, we will subdivide the content into the following sections:
 Starting with Language Model completions and Chat applications and function implementations with language models in .NET.
 
 👉 [Go to part 1 - completions, chat and functions](./01-lm-completions-functions.md)
+
+---
+
+## Want to know more?
+
+📚 **[Detailed Project Documentation](./docs/projects-documentation.md)** - Comprehensive guide covering all sample projects in this lesson, including:
+
+- **Basic Chat Projects** - Learn different approaches to building chat applications with MEAI, Semantic Kernel, and local Ollama models
+- **Functions & Plugins** - Explore function calling, custom plugins, and tool integration across multiple AI frameworks  
+- **RAG (Retrieval-Augmented Generation)** - Discover vector databases, embeddings, and document retrieval with various storage backends
+- **Vision Analysis** - Computer vision capabilities using GitHub Models, Azure OpenAI, and local multimodal models
+- **Audio Processing** - Speech recognition, real-time audio processing, and voice-enabled AI applications
+- **AI Agents** - Agent orchestration, state management, and complex multi-tool workflows
+
+Each project includes detailed dependency analysis, step-by-step setup instructions, demo scenarios, and relevant AI technique tags for easy navigation.