Lenny's PodcastSander Schulhoff: Why role prompting fails on accuracy tasks
Through few-shot examples and self-criticism passes through the model; Sander shows decomposition lifts accuracy from near 0 to 90% on hard reasoning.
Lenny RachitskyhostSander SchulhoffguestGuest (Vanta sponsor segment)guest
CHAPTERS
- 0:00 – 4:56
Introduction to Sander Schulhoff
- 4:56 – 9:01
The importance of prompt engineering
- 9:01 – 12:02
Two modes for thinking about prompt engineering
- 12:02 – 17:30
Few-shot prompting
- 17:30 – 24:52
Prompting techniques to avoid
- 24:52 – 28:26
Decomposition
- 28:26 – 40:29
Self-criticism and context
- 40:29 – 45:59
Ensembling
- 45:59 – 48:23
Thought generation
- 48:23 – 51:56
Conversational vs. product-focused prompt engineering
- 51:56 – 53:37
Introduction to prompt injection and red teaming
- 53:37 – 55:23
AI red teaming competitions
- 55:23 – 1:03:39
The growing importance of AI security
- 1:03:39 – 1:06:17
Techniques to bypass AI safeguards
- 1:06:17 – 1:09:31
Challenges in AI security and future outlook
- 1:09:31 – 1:13:18
Common defenses to prompt injection that don't actually work
- 1:13:18 – 1:16:33
Defenses that do work
- 1:16:33 – 1:19:29
Misalignment and AI's potential risks
- 1:19:29 – 1:26:05
Are LLMs behaving maliciously?
- 1:26:05 – 1:37:46
Final thoughts and lightning round
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