Environment Setup

• Setting up Virtual Environments (venvs)
• Create python projects using uv
• Managing dependencies
• Exploring libraries

Structuring Code

• Functions and Classes
• Strong Typed extensions
• Pydantic & @dataclasses

Logging and Testing

• Implementing basic and configured logging
• Unit and Integration testing

Introduction to LangChain

• Exploring different models & clients (OpenAI, AzureOpenAI, Groq, Local)
• Connecting to models (local & cloud)
• .env file configuration
• Introduction to LangChain Expression Language (LCEL)
• Building a simple chain with Prompts and Models

Working with Documents: Loaders & Splitters

• Loading content from Text/PDF sources
• Understanding LangChain Document structure
• Text splitters and splitting strategies

Embeddings & Vector Stores

• Deep dive into embeddings
• Creating embeddings

Building a RAG System with Chroma

• Understanding embeddings in depth
• Using ChromaDB as local vector store
• Creating and storing embeddings from documents

Advanced Retrieval Strategies

• Beyond similarity search
• MMR & Self-Query retrievers
• Building advanced RAG chains

Introduction to LangGraph

• State, Nodes, Edges
• Building a linear graph
• Conditional logic
• Graph with AI integration

Implementing Agentic Tools

• Defining tools with @tool decorator
• LLM tool calling mechanics
• Testing tools in isolation

Managing State & Memory

• Conversational loops
• Using graph state
• Re-entrancy concepts

Persistence with Checkpointers

• Persistent state importance
• LangGraph Checkpointers
• Using MemorySaver

Composable Agents

• Agents as tools concept
• Designing worker agents
• Packaging agents as tools

Recursive & Hierarchical Agents

• Manager agent delegation
• Recursive workflows
• Problem decomposition

Asynchronous Flow for Performance

• Sync vs Async concepts
• Async methods in LangChain
• Async LangGraph execution

Introduction to LangSmith

• Overview of LangSmith platform

Introduction to Knowledge Graphs

• KG concepts (Nodes, Relationships)
• Neo4j & Cypher
• Populating a simple KG

LangChain & Knowledge Graph Integration

• Text-to-Cypher
• KG-RAG vs Vector-RAG
• KG-based QA system

Streamlit Core Concepts

• Building UI for LangChain
• Interactive chat layouts

Conversational Interfaces with Chainlit

• Chat-first framework
• Layout & element management
• Chainlit vs Streamlit comparison

Advanced UI & Feedback Loop

• User feedback mechanisms
• Capturing feedback
• Handling file uploads for RAG

Interactive Chat Layout

• Persistent chat with gr.ChatInterface
• Connecting UI to LangGraph backend
• Managing state in UI

Introduction to MCP

• MCP architecture
• Consuming MCP in LangChain
• Building first MCP server

Advanced Agent Tools

• Structured tool wrappers
• Async tools
• Error handling

Tool-Based Modality Switching

• Multi-modal agent design
• Router node in LangGraph
• Modality switching logic

Advanced Prompt Engineering

• OpenAI parameters (temperature, top_p)
• System roles
• Chain-of-Thought patterns

Managing Complex Model I/O

• Consistent prompt formats
• Output-to-input chaining
• Output parsing & validation

Handling Multimodal Inputs

• Vision, audio models
• Processing image, PDF, audio inputs

Evaluation Theory & Dataset Creation

• LLM evaluation metrics
• Designing evaluation datasets

Practical Evaluation with LangChain

• Evaluation chains
• Automated evaluation pipeline
• Result analysis

Additional Evaluation Strategies

• Hallucination testing
• Anti-hallucination strategies

The World of Hugging Face

• HF Hub & libraries
• Speech-to-Text use cases

Fine-Tuning

• Why & when to fine-tune
• Preparing datasets

Model Training Loop

• Trainer API setup
• Training configuration
• Saving model artifacts

Evaluating Fine-Tuned Models

• Qualitative vs Quantitative evaluation
• BLEU/ROUGE metrics
• Human-in-the-loop evaluation

Integrating Small & Fine-Tuned Models

• HF Inference API
• LangChain wrapper
• SLM in LangGraph workflow

Azure Functions

• Serverless concepts
• HTTP Trigger endpoint
• Additional triggers & data binding
• Deploying LangChain as web API

Azure AI Search

• From keywords to vectors
• Search architecture
• Index, indexer, pipeline

Professional Project Setup & MLOps

• Project goal tracking
• Git workflow
• CI/CD with GitHub Actions

Introduction to MCP & Azure ML

• AML Workspace setup
• Managing data & model assets

Production Pipelines & Compliance

• Azure-ready ML pipelines with MCP
• Responsible AI compliance
• Monitoring deployed pipelines

Azure AI Studio & Prompt Flow

• Platform overview
• Building visual Prompt Flows
• Integrating LLMs & tools

Advanced Prompt Flow

• Python tools in flows
• A/B testing variants
• Dynamic inputs

Deployment & Evaluation with Prompt Flow

• Built-in evaluation tools
• Model hosting options
• Managed endpoint deployment