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.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (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) OutroLinks:* Riley’s Twitter and LinkedIn* Talk: LLM Prompt Engineering and RLHF: History and Techniques Get full access to The Gradient at thegradientpub.substack.com/subscribe