Nathan hosts Riley Goodside, the world's first staff prompt engineer at Scale AI, to discuss the evolution of prompt engineering. In this episode of The Cognitive Revolution, we explore how language models have progressed, making prompt engineering more like programming than poetry. Discover insights on enterprise AI applications, best practices for pushing LLMs to their limits, and the future of AI automation.
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CHAPTERS:
(00:00:00) About the Show
(00:00:23) Sponsor: WorkOS
(00:01:24) Introduction
(00:06:23) LLMs using LLMs
(00:09:38) Tool Use
(00:11:06) How to manage the breadth of the task
(00:14:51) Prompt engineering
(00:26:49) Multitasking fine-tuning
(00:31:49) Sponsors: Omneky | Squad
(00:33:36) Best models for fine-tuning
(00:36:41) The Platonic Representation Hypothesis
(00:42:02) How close are we to AGI?
(00:45:44) How do you know if youre being too ambitious?
(00:51:18) Best practices for generating good output
(00:54:33) Backfills and synthetic transformations
(00:56:59) Prompt engineering
(01:05:54) AGI, modalities, and the limits of training
(01:20:08) Solving the ARC Challenge
(01:23:20) How to Demonstrate Prompt Engineering Skills
(01:25:27) Outro
Today we’re joined by Riley Goodside, staff prompt engineer at Scale AI. In our conversation with Riley, we explore LLM capabilities and limitations, prompt engineering, and the mental models required to apply advanced prompting techniques. We dive deep into understanding LLM behavior, discussing the mechanism of autoregressive inference, comparing k-shot and zero-shot prompting, and dissecting the impact of RLHF. We also discuss the idea that prompting is a scaffolding structure that leverages the model context, resulting in achieving the desired model behavior and response rather than focusing solely on writing ability.
The complete show notes for this episode can be found at twimlai.com/go/652.
ChatGPT can now hear, see and speak — and that’s just the start of the deluge of A.I. news this week. Kevin and Casey unpack the lightning-speed updates.
Then, Meta’s next-generation headset, Quest 3, is here. Is there still hope for the metaverse?
And: An interview with a prompt engineer. Yes, that’s a real job.
Today’s Guest:
Riley Goodside is a prompt engineer at Scale A.I., a San Francisco start-up.
Additional Reading:
Kevin Roose on ChatGPT, which can now see, hear and speak.
Spotify announced a new A.I.-powered voice-translation feature.
Meta announced the release of the Quest 3 headset.
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Full Transcript and show notes: https://www.latent.space/p/function-agents?sd=pf
Timestamps:
[00:00:00] Intro
[00:01:47] Recapping June 2023 Updates
[00:06:24] Known Issues with Long Context
[00:08:00] New Functions API
[00:10:45] Riley Goodside
[00:12:28] Simon Willison
[00:14:30] Eric Elliott
[00:16:05] Functions API and Agents
[00:18:25] Functions API vs Google Vertex JSON
[00:21:32] From English back to Code
[00:26:14] Embedding Price Drop and Pinecone Perspective
[00:30:39] Xenova and Huggingface Perspective
[00:34:23] Function Selection
[00:39:58] Designing Code Agents with Function API
[00:42:16] Models as Routers
[00:46:48] Prompt Engineering replaced by Finetuning
[00:52:15] The 2 Code x LLM Paradigms
[00:56:30] Smol Models for the future
[00:58:54] The Evolution of the GPT API
[01:03:27] Functions API Security vs Prompt Injection
[01:16:18] GPT Model Upgrades
[01:17:36] JSONformer
[01:21:03] Closing Comments - What We Want Next
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe
This is a special preview episode of The Cognitive Revolution: How AI Changes Everything. Hosted by Erik Torenberg and Nathan Labenz, TCR hosts in-depth interviews with the creators, builders and thinkers pushing the bleeding edge of AI. On this episode, they talk with Riley Goodside, the first Staff Prompt Engineer at Scale AI and expert in prompting LLMs and integrating them into AI applications.
Check out The Cognitive Revolution The perfect AI interview complement to The AI Breakdown https://link.chtbl.com/TheCognitiveRevolution Find TCR on YouTube: https://www.youtube.com/@CognitiveRevolutionPodcast
For anyone who discovered this show on Twitter, Riley (@goodside) likely needs no introduction – after all, he spent most of 2022 posting his explorations of OpenAI's text-davinci-002, and quickly became one of the must-follow accounts in AI. Riley Goodside is the world's first Staff Prompt Engineer at Scale AI, and is an expert in prompting large language models and integrating them into AI-powered applications. Few have spent as much time on the language model frontier, so I hope you enjoy this unique conversation with Riley Goodside.
We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.
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TIMESTAMPS:
(0:00) Preview of the episode
(05:13) Riley's unique background
(11:24) Riley's original narrow moat
(15:07) Sponsors: Omneky
(17:02) LLMs can take on ncreasingly complex instructions
(27:41) Language models can do math
(30:05) Models can learn from context
(37:29) Fine-tune models for tasks
(38:47) Automate instruction following
(44:34) Large language models are alien text prediction
(52:27) Avoid mode collapse by framing
(59:05) Composing AI capabilities like Lego bricks
(1:00:37) Language models solve tasks
(1:05:37) GPT-3 solves real-world tasks
(1:15:03) GPT-4's amazing capabilities
(1:17:03) Multimodal abilities unlock possibilities
(1:25:03) Compare models for best task fit
(1:26:24) Compare models using trial and error
(1:36:15) AI and the future of work
(1:42:29) GPT-4 can provide second opinions
(1:45:06) AI safety discussion
(1:50:20) AI permeates society cautiously
(1:55:26) The AI revolution underway
TWITTER:
@CogRev_Podcast
@Goodside (Riley)
@scale_AI (Scale)
@labenz (Nathan)
SPONSORS:
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More show notes and reading material released in our Substack: https://cognitiverevolution.substack.com/