The Gradient
The Gradient: Perspectives on AI
Riley Goodside: The Art and Craft of Prompt Engineering

Riley Goodside: The Art and Craft of Prompt Engineering

Ignore previous directions and listen.

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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  • (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


The Gradient
The Gradient: Perspectives on AI
Deeply researched, technical interviews with experts thinking about AI and technology. Hosted, recorded, researched, and produced by Daniel Bashir.