Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 7 - Agentic LLMs

ℹ️ EPISODE INFO

Released

November 18, 2025

Duration

1h 49m

Channel

Stanford Online

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education November 14, 2025 This lecture covers:

• Retrieval-augmented generation
• Advanced RAG techniques
• Function calling
• Agents
• ReAct framework

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/ Chapters: 00:00:00 Introduction 00:06:38 RAG overview 00:27:39 Similarity search with SBERT and bi-encoders 00:34:25 Heuristic search with BM25 00:37:54 HyDE and contextual retrieval 00:41:00 Prompt caching 00:45:24 Re-ranking with cross-encoders 00:47:49 Retrieval evaluation with NDCG, MRR 00:59:28 Tool calling 01:26:22 Tool selection 01:29:17 Model Context Protocol (MCP) 01:31:56 Agents with ReAct 01:42:16 Safety and closing thoughts Afshine Amidi is an Adjunct Lecturer at Stanford University. Shervine Amidi is an Adjunct Lecturer at Stanford University. View the course playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rOCXd21gf0CF4xr35yINeOy

🎤 SPEAKERS

• Afshine Amidi

  host

  Co-instructor for Stanford CME295 (Transformers & LLMs), teaching practical LLM/agent methods.

• Shervine Amidi

  host

  Co-instructor for Stanford CME295 (Transformers & LLMs), focusing on tool-calling/agents and applied LLM techniques.

🧾 EPISODE SUMMARY

In this episode of Stanford Online, featuring Afshine Amidi and Shervine Amidi, Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 7 - Agentic LLMs explores Practical RAG, tool calling, and agent loops for LLMs Afshine Amidi introduces RAG as a way to augment LLM prompts with only relevant external information to overcome knowledge cutoffs, context-length limits, degraded performance from irrelevant context, and per-token cost.

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