Unlocking the Full Potential of LLMs: Advanced Prompt Engineering Techniques
summary
Claims that combining chain-of-draft with chain-of-thought compresses long reasoning (she shows 100-step runs summarized to a six-word output) while saving tokens, demonstrates ReAct calling external search and databases to update facts (example: iteratively counting band members), and uses self-consistency by running prompts multiple times to select the most common answer. Shows concrete prompt patterns—role-based instructions, JSON output requirements, zero/few-shot examples—and quantifies the tradeoff: transparent, costly chain-of-thought versus faster, opaque standard prompts.
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chapters (11)
- 0:00Introduction to prompt engineering
- 1:04Foundations of prompt engineering
- 4:15Zero-shot and few-shot techniques
- 5:40Role-based prompt design
- 9:13Balancing speed and cost
- 9:36Chain of thought prompting
- 12:04Tree of thoughts technique
- 13:09REACT framework explained
- 14:58Self-consistency in prompts
- 16:03Meta prompting overview
- 17:30Advanced strategies summary
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