InfoTekGuide

Course Content

 

Course Content:

 

🔹 Week 1: Foundations of AI & LLMs (3.5 hrs)

Topics:

• AI/ML/Deep Learning refresher

• What are Large Language Models (LLMs)

• Transformer architecture basics

• Types of LLMs (OpenAI, Claude, Mistral, LLaMA)

• LLM capabilities and limitations

• Use cases in business: copilots, summarization, recommendation

Lab: Run an LLM via API (OpenAI or Hugging Face)
Quiz: AI/LLM fundamentals
Outcome: Understand LLM architecture and business context

 

🔹 Week 2: Prompt Engineering Essentials (3.5 hrs)

Topics:

• Prompt structure: instructions, context, input

• Prompting techniques: zero-shot, few-shot, chain-of-thought

• System vs. user prompts

• Role prompting and format engineering

• Evaluation and optimization of prompts

Lab: Create and compare various prompt styles (OpenAI Playground)
Quiz: Prompt design patterns
Outcome: Build optimized prompts for common LLM tasks

 

🔹 Week 3: Retrieval-Augmented Generation (RAG) (3.5 hrs)

Topics:

• What is RAG? Why use it?

• RAG architecture (vector store + LLM)

• Embeddings (OpenAI, Hugging Face, Cohere)

• Vector databases (FAISS, Pinecone, Chroma)

• Chunking, indexing, retrieval strategies

Lab: Build a basic RAG pipeline with PDF ingestion
Quiz: RAG architecture and benefits
Outcome: Retrieve and answer domain-specific content using RAG

 

🔹 Week 4: Introduction to Agentic AI (3.5 hrs)

Topics:

• What are AI Agents?

• Autonomous vs. tool-using agents

• Agent loop: plan → execute → observe → reflect

• Tools & frameworks: LangChain agents, OpenAI tools/functions, AutoGPT

• Real-world agent use cases: research, coding, data pipelines

Lab: Build a tool-using agent with LangChain + OpenAI
Quiz: Agent types and workflows
Outcome: Understand and build your first AI agent​​