Here is the LLM Course Agenda

**LLM Course Agenda **

Module 1: Foundations

1. What are LLMs? (history, evolution, GPT, Claude, LLaMA, etc.)
2. Key terminology: tokens, embeddings, fine-tuning, context windows
3. Open vs. closed-source models and licensing considerations

Module 2: LLM Architecture

1. Transformer architecture deep dive

   • Attention mechanism (self-attention, multi-head attention)
   • Encoder vs. decoder vs. encoder-decoder LLMs
   • Position embeddings and tokenization

2. Training lifecycle

   • Pretraining, instruction tuning, reinforcement learning with human feedback (RLHF)
   • Scaling laws

3. Serving LLMs

   • Latency, inference optimizations, quantization, GPU vs. CPU hosting

Module 3: Prompt Engineering

1. Prompt design: zero-shot, one-shot, few-shot
2. Advanced prompting: chain-of-thought, self-consistency, role prompting
3. Practical exercises: summarization, Q&A, structured outputs
4. Debugging and evaluation

Module 4: Working with LLM APIs

1. OpenAI, Anthropic, Cohere, open-source APIs
2. Tokens, costs, and response formatting (JSON, XML, structured)
3. Streaming responses, function calling, and tool use

Module 5: Retrieval-Augmented Generation (RAG)

1. Why RAG vs. fine-tuning?
2. Embeddings and vector databases (Pinecone, FAISS, Weaviate)
3. Document chunking and indexing strategies
4. Query pipelines: hybrid search, reranking, context injection
5. Hands-on: build a RAG system end-to-end

Module 6: Fine-Tuning & Customization

1. Fine-tuning vs. RAG vs. prompt engineering
2. LoRA & parameter-efficient fine-tuning
3. Custom instructions & personality tuning
4. Hands-on fine-tuning exercise

Module 7: Applications of LLMs

1. Conversational AI Assistants (customer service, personal assistants, productivity tools)
2. Insight Generation from Structured Data (dashboards, analytics, financial insights)
3. Tax Research Assistant (OCR → JSON extraction → RAG on IRS code)
4. Legal Document Analysis (clause extraction, contract comparison, compliance checks)
5. Healthcare & Education (medical summarization, tutoring assistants)
6. Coding & Dev Tools (copilots, debugging assistants)
7. Industry-specific assistants (finance, HR, real estate, government)

Module 8: Safety, Ethics & Governance

1. Bias, fairness, and hallucinations
2. Guardrails and content moderation
3. Data privacy & compliance (HIPAA, GDPR, IRS-related data)
4. Responsible deployment practices

Module 9: Capstone Project

• Build an end-to-end assistant: ingestion, RAG/fine-tuning, prompting, evaluation

• Example domains:

  • Tax research assistant
  • Legal contract analyzer
  • Financial insight generator
  • Personalized productivity assistant

• Demo, architecture diagram, lessons learned

✅ By the end of the course, learners will:

• Understand LLM architectures deeply
• Be able to prompt, evaluate, and improve outputs
• Build RAG pipelines and simple fine-tuned models
• Design real-world AI applications responsibly