Advanced Prompt Engineering and Beyond
summary
Claims specific performance gains from techniques and clear failure modes for production: shows retrieval pipelines using Hyde and ColBERTv2 and re‑ranking, compares Chain of Thought prompting versus zero‑shot on a small math subset (accuracy varies), describes RAG with vector embeddings, hybrid BM25 re‑rank, and MCP/tool patterns for coding agents, gives a biotech PEFT use case (antimicrobial peptides), reports latency and cost tradeoffs, and documents when pipelines actually work, when they hallucinate, and what needs more labels or tuning.
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chapters (30)
- 0:00Prompt engineering and LLM products
- 1:06Simple prompts often fail
- 2:11Zero-shot and role-based prompts
- 3:13Tricky prompts and chain-of-thought
- 4:18Non-deterministic model outputs
- 6:05LLM as judge for evaluation
- 7:08LLM randomness and token probabilities
- 8:57External knowledge for LLMs
- 10:42Retrieval Augmented Generation limits
- 12:07Vector embeddings and limits
- 14:34Alternatives to vector embeddings
- 16:48Measuring retrieval performance
- 18:55Long conversations and context shortening
- 20:01Testing LLM reasoning with games
- 23:56LLM-based games limitations
- 24:18How coding agents work
- 25:45Tips for coding agents
- 26:29Introduction to MCP
- 28:44MCP connects models to APIs
- 30:29MCP cost and context window
- 30:52Fine-tuning LLMs
- 33:26When fine-tuning is worth it
- 34:07Challenges training from scratch
- 34:48LLM application traps
- 35:31Privacy with public LLMs
- 35:52Cost analysis of LLMs
- 36:54Open-source LLM costs
- 37:15Risks in LLM projects
- 37:37Experimentation vs business goals
- 38:19Q&A on prompt engineering and RAG
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