Advanced Topics¶
Where LLMs meet production. Retrieval-augmented generation, agents, long-context engineering, multimodal models, scaling laws, evaluation, and safety.
Goals¶
After completing this section you will be able to:
- Design a production RAG pipeline with chunking, embedding, retrieval, re-ranking, and evaluation
- Build an LLM agent with tool calling, memory, and a reasoning loop
- Explain FlashAttention, sliding window attention, and RoPE scaling for long contexts
- Describe how multimodal models connect vision encoders to language decoders
- Design an evaluation pipeline combining benchmarks, human evaluation, and LLM-as-judge
- Implement guardrails for hallucination detection and safety enforcement
Topics¶
| # | Topic | What You Will Learn |
|---|---|---|
| 1 | RAG | Chunking, embedding, vector DBs, re-ranking, evaluation |
| 2 | Agents and Tool Use | ReAct, function calling, planning, multi-agent systems |
| 3 | Long-Context Modeling | FlashAttention, sliding window, RoPE scaling, sparse attention |
| 4 | Multimodal LLMs | CLIP, ViT, LLaVA, Gemini, vision-language fusion |
| 5 | Emergent Capabilities | Scaling laws, in-context learning, CoT, test-time compute |
| 6 | Evaluation and Benchmarking | MMLU, HumanEval, Chatbot Arena, LLM-as-judge |
| 7 | Hallucination and Safety | Detection, mitigation, red-teaming, guardrails |
Every page includes plain-English math walkthroughs, worked numerical examples, runnable Python code, and FAANG-level interview questions.