The Relationship Between Prompts and Intext Learning

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.

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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