"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis
E17: The Prompt Engineering Revolution with Riley Goodside
Apr 18, 2023
Riley Goodside, the world's first Staff Prompt Engineer at Scale AI, dives into the art of prompt engineering and its critical role in enhancing AI interactions. He shares insights from his journey in data science, emphasizing the importance of clarity in crafting effective prompts. The conversation also covers the evolving capabilities of language models like GPT-4 and the nuanced landscape of AI advancements. Goodside discusses both the practical applications and future implications of AI technologies, advocating for a balanced understanding of their potential and limitations.
01:58:20
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Quick takeaways
- Large language models have undergone different phases of development, starting with text prediction and progressing to instruction tuning and RLHF, which has improved their ability to provide correct answers.
- Despite their impressive language understanding and text generation capabilities, large language models like GPT-3 rely on extensive training data and techniques like instruction tuning and reinforcement learning, rather than true understanding or consciousness.
Deep dives
The Evolution of Language Models
Language models have undergone different phases of development. Initially, models relied on text prediction, where they attempted to complete text based on preceding context. This was known as the pre-trained era. Then came the phase of instruction tuning, where models were fine-tuned on examples to follow instructions. This improved their ability to perform specific tasks. The current phase is RLHF (Reinforcement Learning from Human Feedback), where models generate their own answers and receive human feedback to optimize their performance. This phase allows for more automated task completion and has improved models' capability to provide correct answers.
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