Mastering Large Language Models: Prompt Engineering for Precision & Performance
summary
Claims prompt engineering recovers developer time by making LLMs more predictable and useful, citing that developers spend about 84% of the day on noncoding tasks and often write code less than a quarter of their time. Demonstrates tools and integrations: OpenAI, Google Vertex, Anthropic, GitHub Copilot, Claude, and an Atlassian product called robo product integrates agents, search, and chat. Shows experimentation results where Cloud Sonnet outperforms Copilot for certain debugging tasks and spells out concrete techniques: zero/few-shot, chain-of-thought, clear instructions, output length, audience, and separators.
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chapters (7)
- 0:00Introduction
- 2:53Why prompt engineering matters
- 7:26What is prompt engineering
- 11:16Prompt types explained
- 14:52Better prompt strategies
- 20:29Prompt experiments
- 23:42Best practices & summary
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