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Prompt Engineering | Lil'Log

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Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation and heuristics. This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models.

Table of Contents Basic Prompting Zero-Shot Few-shot Tips for Example Selection Tips for Example Ordering Instruction Prompting Self-Consistency Sampling Chain-of-Thought (CoT) Types of CoT prompts Tips and Extensions Automatic Prompt Design Augmented Language Models Retrieval Programming Language External APIs Citation Useful Resources References Prompt Engineering , also known as In-Context Prompting , refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering

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