In this course, you'll learn:

• Agentic AI Primer (Core Concepts & Terminology)
  • Defining agentic systems and how they differ from standard automation
  • Agentic components: memory, tools, and reasoning loops
  • Comparing agentic workflows with RAG and fine-tuning
  • Shared terms: planner, executor, environment, guardrails 
• Anatomy of a Conversational Agent
  • Conversational agents vs. chatbots
  • Key LangChain components and agent workflows
  • Creating a minimal  LLMChain
• Prompt Engineering for Dialogue
  • System and user prompt design
  • Tone matching and safety phrasing
  • Few-shot prompting and verbosity control
• Conversational Memory
  • Short-term vs. summary memory 
  • Privacy tradeoffs and memory configs
  • How memory impacts response quality
• Vector Search & Document Embedding
  • Embeddings and vector search basics
  • Using FAISS and chunking strategies
  • Document retrieval with OpenAI models
• Retrieval-Augmented Generation (RAG)
  • How RAG improves accuracy and grounding
  • Using LLamaIndex with LangChain
  • When to prefer RAG over direct embedding
• External Tool Integration & Function Calling
  • Tools vs. APIs in LangChain
  • Function calling and controlled execution
  • Routing inputs and chaining results
• Evaluations & Guardrails
  • Latency targets for user experience
  • Token usage monitoring and cost control
  • Content filtering and fallback design
• Deploying via Hugging Face Spaces
  • Deployment steps and required files
  • Streamlit vs. Gradio UI basics
  • Secret and token management