Master system prompts, few-shot techniques, chain of thought reasoning, and structured output.
System prompts define how your LLM behaves. Here's how to structure them, what mistakes to avoid, and how provider-specific behavior affects your prompt strategy.
Few-shot prompting teaches LLMs by example instead of instruction. Here's how to choose examples, format them, and know when few-shot is the right approach vs. fine-tuning.
Chain of thought prompting makes LLMs reason through problems step by step. Here's when it works, when it doesn't, and how to implement it with practical patterns.
LLMs generate text, but applications need structured data. Here's how JSON mode, function calling, and schema enforcement turn free-form AI output into reliable, typed data.
Prompting isn't about magic phrases. It's structured thinking that determines output quality. Here's how to write prompts that actually work, from frameworks to chain-of-thought to system prompts.
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Build agents with tool use, memory, multi-agent orchestration, and evaluation frameworks.
Get Insanely Good at AI
Chapter 3: Prompting Is Thinkingtreats prompting as structured thinking, not templates. System prompts, few-shot reasoning, chain-of-thought, and the iteration process that turns prompting into a real skill.
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