Prompt engineering remains essential for extracting optimal performance from transformer-based LLMs without parameter updates. Core techniques include Chain-of-Thought (CoT) prompting, which decomposes complex problems into intermediate reasoning steps, and Tree-of-Thoughts (ToT), which implements tree search over multiple reasoning trajectories.
Key Uses:
In software engineering, few-shot and role-based prompts enhance code generation accuracy in models like GPT-4 or Claude. In healthcare, structured prompts improve named entity recognition (NER) from clinical notes.
Hacks:
Use delimiters (XML-style tags) to prevent prompt injection. Use delimiters (XML-style tags) to prevent prompt injection.
Implement prompt chaining for multi-step workflows. Apply Automatic Prompt Engineer (APE) where a meta-LLM iteratively optimizes prompts using evolutionary search. Adjust temperature (0.0–1.0) and top-p nucleus sampling for creative vs deterministic outputs.
References:
Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS.
Yao, S., et al. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models.