Here is LLM Course Agenda into a 5-day intensive workshop with daily focus, clear goals, and hands-on labs.

5-Day Intensive LLM Workshop

Day 1 – Foundations & Architecture of LLMs

Goals:

• Understand what LLMs are, how they evolved, and where they are used
• Grasp the core architecture and training process of transformers

Topics:

• Introduction to LLMs (history, evolution, ecosystem)
• Key terminology (tokens, embeddings, context windows)
• Transformer architecture: attention, tokenization, positional encoding
• Pretraining, instruction tuning, RLHF
• LLM serving: inference, latency, quantization

Hands-On Lab:

• Visualize transformer attention with a demo notebook
• Compare outputs from different LLMs (OpenAI GPT vs. open-source model)

Day 2 – Prompt Engineering & API Mastery

Goals:

• Learn how to communicate effectively with LLMs
• Master prompt patterns for structured and reliable outputs

Topics:

• Zero-shot, one-shot, few-shot prompting
• Chain-of-thought, self-consistency, role prompting
• Working with APIs (OpenAI, Anthropic, Cohere, open-source models)
• Tokens, costs, response formatting (JSON, XML, structured output)
• Streaming, function calling, tool use

Hands-On Lab:

• Write prompts for summarization, extraction, and insight generation
• Build a simple chatbot using an API

Day 3 – Retrieval-Augmented Generation (RAG)

Goals:

• Understand why RAG is critical for real-world apps
• Build a working RAG pipeline from scratch

Topics:

• Why RAG vs. fine-tuning?
• Embeddings & vector databases (Pinecone, FAISS, Weaviate)
• Document chunking & indexing strategies
• Hybrid search, reranking, and context injection

Hands-On Lab:

• Ingest documents, generate embeddings, store in vector DB
• Query and inject retrieved context into LLM responses

Day 4 – Fine-Tuning & Applications of LLMs

Goals:

• Learn when to fine-tune vs. prompt vs. RAG
• Explore diverse real-world applications

Topics:

• Fine-tuning basics: LoRA, PEFT, supervised fine-tuning

• Custom instructions & personality tuning

• Applications:

  • AI Assistants
  • Insight generation from structured data
  • Tax research assistant
  • Legal clause extraction
  • Coding copilots & analytics assistants

Hands-On Lab:

• Fine-tune a small LLM with sample domain data
• Prototype a specialized assistant (choose finance, legal, or healthcare)

Day 5 – Safety, Governance & Capstone Project

Goals:

• Build and present an end-to-end LLM application
• Understand responsible AI deployment practices

Topics:

• Bias, hallucinations, and evaluation
• Guardrails & moderation strategies
• Privacy & compliance (HIPAA, GDPR, IRS data)
• Deployment architectures & monitoring

Capstone Project:

• Teams design an end-to-end LLM solution:

  • Ingest data → RAG/fine-tuning → Prompt design → Evaluation
  • Example: Tax assistant, Legal contract analyzer, or Data insight generator

• Present demo, architecture, and lessons learned

✅ By the end of Day 5, participants will:

• Understand LLMs from architecture to deployment
• Be skilled in prompting, RAG, and fine-tuning
• Build and demo a real-world AI assistant responsibly