Prompt Engineering for LLMs: A Deep Dive

A practical tour of prompt engineering for large language models. We cover what prompts are and how model settings like max tokens, temperature, top-k, and top-p shape outputs. Explore zero-shot, one-shot, and few-shot prompting, plus system, contextual, and role prompts. We also dive into advanced techniques like step-back prompts and chain-of-thought prompting, and discuss getting structured JSON outputs. Aimed at coders and AI practitioners looking to make LLMs more reliable, cost-efficient, and problem-solving focused.

Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

Sponsored by Embersilk LLC