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In episode 75 of The Gradient Podcast, Daniel Bashir speaks to Riley Goodside. Riley is a Staff Prompt Engineer at Scale AI. Riley began posting GPT-3 prompt examples and screenshot demonstrations in 2022. He previously worked as a data scientist at OkCupid, Grindr, and CopyAI. Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pub Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter Outline: * (00:00) Intro * (01:37) Riley’s journey to becoming the first Staff Prompt Enginer * (02:00) data science background in online dating industry * (02:15) Sabbatical + catching up on LLM progress * (04:00) AI Dungeon and first taste of GPT-3 * (05:10) Developing on codex, ideas about integrating codex with Jupyter Notebooks, start of posting on Twitter * (08:30) “LLM ethnography” * (09:12) The history of prompt engineering: in-context learning, Reinforcement Learning from Human Feedback (RLHF) * (10:20) Models used to be harder to talk to * (10:45) The three eras * (10:45) 1 - Pre-trained LM era—simple next-word predictors * (12:54) 2 - Instruction tuning * (16:13) 3 - RLHF and overcoming instruction tuning’s limitations * (19:24) Prompting as subtractive sculpting, prompting and AI safety * (21:17) Riley on RLHF and safety * (24:55) Riley’s most interesting experiments and observations * (25:50) Mode collapse in RLHF models * (29:24) Prompting models with very long instructions * (33:13) Explorations with regular expressions, chain-of-thought prompting styles * (36:32) Theories of in-context learning and prompting, why certain prompts work well * (42:20) Riley’s advice for writing better prompts * (49:02) Debates over prompt engineering as a career, relevance of prompt engineers * (58:55) Outro Links: * Riley’s Twitter and LinkedIn * Talk: LLM Prompt Engineering and RLHF: History and Techniques
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