Application Development with Generative AI and Agentic AI

Fine-Tuning LLMs

• Fine-tuning fundamentals (Open in AI & Hugging Face models)
• Parameter-efficient techniques (LoRA, ReFT)
• Domain-specific fine-tuning best practices
• Hands-on: Fine-tune GPT models on domain-specific datasets

Prompt Engineering Mastery

• Advanced prompt structures for accuracy
• Dynamic prompt generation for contextual results
• Reducing hallucinations via precise prompting
• Hands-on: Developing robust, production-grade prompts

Evaluation & Optimization

• Key metrics for AI model performance
• Preventing overfitting in fine-tuned models
• Cost optimization strategies for API consumption
• Hands-on: Model performance evaluation and cost management

Integration with Enterprise Applications 

• Integrating fine-tuned models with .NET Core apps
• Python-based API deployment
• Dockerizing and Kubernetes deployment strategies
• Hands-on: Deployment of fine-tuned models into existing apps

Managing Data Privacy in Fine-Tuning

• Data anonymization and privacy considerations
• Secure handling of sensitive data
• Hands-on: Applying privacy strategies in fine-tuning pipelines

RAG Foundations & Enterprise Applications

• RAG concepts for accuracy and context-awareness
• Semantic vs. keyword search benefits
• Business-critical RAG implementations

Vector Embeddings and Semantic Search

• Embedding models (OpenAI, Hugging Face)
• Generating embeddings with Python (NumPy)
• pgvector with PostgreSQL: Embedding storage & retrieval
• Hands-on: Implement semantic search pipelines

Advanced RAG Implementation

• Building RAG agents with LLMs
• Chain building
• Document Reasoning
• LlamaIndex integration for structured retrieval
• Intent extraction & contextual retrieval strategies
• LLM as a JUDGE
• Reducing hallucinations through improved retrieval
• Hands-on: Build an advanced RAG-based knowledge assistant

Hybrid Search Techniques

• Combining semantic and keyword searches
• Optimization techniques for hybrid retrieval
• Query tuning and indexing optimizations
• Hands-on: Hybrid search implementation with PostgreSQL & pgvector

Scalability & Performance Optimization

• Addressing latency issues in large datasets
• Scaling RAG with MongoDB Atlas or PostgreSQL clusters
• Performance benchmarking and tuning
• Hands-on: Scaling and optimizing RAG pipelines

RAG Evaluation

• • RAG evaluation
  • Look over RAGAS
  • Evaluation chain components

AI Agents: Core Concepts

• Types of AI Agent Architectures
• Fundamentals of autonomous AI agents (BDI, ReAct)
• Understanding reasoning, memory, and planning in agents
• Agent-based vs. traditional automation workflows
• Real-world use cases in enterprise environments
• Microservices Architecture for AI Agents.
• Event Driven Architectures in AI Agent Systems
• Practical Implementation and labs 

AI Agent Frameworks and Tools

• LangChain: Agent creation and workflow automation
• LlamaIndex: Structured retrieval for agent context
• Microsoft Autogen: Multi-agent collaboration
• The HULA framework: Bridging Automation and Human Expertise in Software Development.
• Current Human-in-the-loop solutions
• GotoHuman
• Get started with the Model Context Protocol (MCP)
• Hands-on: Develop a simple agentic workflow with LangChain

Advanced Application Development with LangGraph

• Introduction to LangGraph
• LangGraph Architecture & Concepts
• Integrating Generative AI with LangGraph
• Advanced Agentic AI with LangGraph
• Scalability and Optimization Techniques
• Integrating LangGraph into Enterprise Ecosystems
• Monitoring, Debugging, and Maintenance
• Security, Privacy, and Compliance in LangGraph Applicationsenhancements in workflows
• Best Practices & Case Studies
• Hands-on: Implementing a generative AI node for context-aware content generation
• Hands-on: Building and deploying a multi-agent workflow

Multi-Agent Systems (MAS)

• Importance of AI Infrastructure for scaling AI agent systems.
• Roadblocks to building AI Agentic Systems.
• Designing multi-agent interactions and workflows
• Connecting AI agents for system
• Communication, conflict resolution among agents
• Event-driven architectures for agents
• Hands-on: Build a multi-agent system for event-driven automation

Agent Integration and Scalability

• Building extendable agent infrastructures
• Integration with .NET Core, Python backend systems
• Scalability considerations in agent deployment
• Hands-on: Create scalable agent architecture integrating with Docker & Kubernetes

Monitoring, Debugging & Optimization

• Common agent pitfalls and troubleshooting methods
• Real-time monitoring and logging techniques
• Performance optimization strategies for agents
• Hands-on: Optimize and troubleshoot agent workflows

Building Scalable RAG Pipelines and Chatbots with LCEL

• Introduction to LCEL
• Implementing and Managing State Chains
• Efficient Document Ingestion Techniques
• Generating and Utilizing Embeddings
• Optimized Document Retrieval Strategies
• Evaluating RAG Systems Effectively
• Developing Conversational Chatbots with Dialogue Management and RAG
• Constructing and Deploying End-to-End Pipelines with Foundation Models at Scale

AI-Assisted Coding & Debugging

• GitHub Copilot, Amazon CodeWhisperer integrations
• Automating code generation and debugging
• Effective AI-assisted pair programming
• Hands-on: Integrate AI tools into VS Code/Visual Studio workflows

Automating Testing & Quality Assurance

• Generating and automating unit/integration tests
• AI-powered code review and quality analysis
• Automated bug detection and resolution
• Hands-on: Automating tests with AI-driven workflows

Documentation & Report Automation

• AI-generated project documentation
• Intelligent report summarization
• Automating report creation pipelines
• Hands-on: Implement AI-driven documentation workflows

DevOps & CI/CD Automation with AI

• Deployment stack
• AI-driven error detection and CI/CD automation
• AI-assisted incident management and DevOps tasks
• Hands-on: Build AI-integrated DevOps pipelines (GitHub Actions, Azure DevOps)

Cross-platform AI Integration

• Bridging Python-based AI with .NET Core apps
• RESTful APIs for multi-platform AI interactions
• Hands-on: Develop integrated cross-platform AI solutions

Ethical AI & Responsible Practices

• Bias detection and mitigation in generative models
• Adobe and Google ethical AI case studies
• Explainability (XAI) tools (SHAP, LIME)
• Hands-on: Ethical evaluation and bias reduction labs

Security in AI Implementations

• Common security threats (prompt injections, adversarial attacks)
• Secure deployment of AI models and APIs
• Security best practices (data encryption, secure endpoints)
• Hands-on: Conduct security assessments on AI workflows

Data Privacy & Compliance

• GDPR, HIPAA, EU AI Act compliance essentials
• Data anonymization and masking techniques
• Compliance audit preparation
• Hands-on: Implement privacy-compliant AI systems

AI Governance & Model Lifecycle

• AI governance frameworks and committees
• Secure AI development lifecycle management
• Audit trails, monitoring, and logging
• Hands-on: Build an AI governance plan for enterprise deployment

Operationalizing Ethical AI at Scale

• Ensuring ethical alignment in production
• Continuous monitoring and ethics audits
• Case studies: Enterprise AI ethics implementations
• Hands-on: Ethics audit of final capstone projects

Participants collaboratively develop a complete AI-driven enterprise application covering:

• Advanced fine-tuning & prompt engineering (Day 1)
• Autonomous AI agents (Day 2)
• RAG & semantic search optimizations (Day 3)
• AI-driven software automation (Day 4)
• Ethical AI, security, and compliance (Day 5)

The capstone reflects real-world enterprise scenarios, optimized for scalability, accuracy, ethical standards, and seamless backend integration (.NET Core & Python).