Applied LLMs: Architecture, Reasoning & AI Agents

⏱️Hour 1: LLM Foundations & Text Generation

Topics Covered

• What is a Large Language Model?
• LLM intro & evolution
• How LLMs work (high-level pipeline)
• Tokenization & next-token prediction
• LLM text generation
  • Temperature, Top-K, Top-P
• LLM improvements
  • Instruction tuning
  • RLHF (overview)

⏱️ Hour 2: Retrieval Augmented Generation (RAG)

Topics Covered

• Why LLMs hallucinate
• RAG vs fine-tuning
• RAG architecture (end-to-end)
• Embeddings & vector search
• Chunking & retrieval strategies
• Common RAG failures

Practical Angle

• When RAG is mandatory
• When RAG does not help

⏱️ Hour 3: LLM Internals & Transformer Architecture

Topics Covered

• Transformer architecture (decoder-only)
• Attention: what it is & why it matters
• Positional embeddings (absolute, relative, RoPE)
• Masked attention
• Multi-head attention
• KV Cache
  • Why it reduces latency
  • Memory tradeoffs

⏱️ Hour 4: Agents & MCP (High-Impact)

Topics Covered

Agents & MCP

• Context engineering
• AI agents (planner, executor, memory)
• Multi-agent workflows
• Model Context Protocol (MCP)
• Real-world agent use cases
• Flash, Paged & Sparse Attention
• Mixture of Experts (MoE)

⏱️ Hour 5: Reasoning, Core Optimizations & Tradeoffs

Topics Covered

Reasoning in LLMs

• Reasoning models with human feedback
• Chain of Thought (CoT)
• Tree of Thought (ToT)
• Tool usage in LLMs
• When to expose vs hide reasoning
• Core optimizations comparison
• Tradeoffs in LLMs:
  • Cost vs accuracy
  • Latency vs throughput
  • Context length vs performance

Wrap-up

• How all components fit together
• Career & interview relevance
• What to learn next

🎯 Final Takeaways for Attendees

participants will:

• Understand LLMs from internals to production
• Confidently discuss attention, transformers & optimizations
• Design RAG + agent-based systems
• Understand reasoning & tool-using models
• Be interview-ready for GenAI / AI system roles

💡 Why This Format Works

• No fluff
• System-level thinking
• Covers 100% of your listed topics