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Prompt Engineering | Lil'Log
Source: lilianweng.github.io
May be slightly imbalanced

Article summary:

1. Prompt Engineering, also known as In-Context Prompting, is a method for communicating with LLM to steer its behavior for desired outcomes without updating the model weights.

2. The goal of prompt engineering is about alignment and model steerability, and it requires heavy experimentation and heuristics.

3. Prompt engineering can be used for autoregressive language models through basic prompting, instruction prompting, self-consistency sampling, and chain-of-thought prompting. Tips and extensions include example selection and ordering, diverse sample trials, and fine-tuning with generated rationales.

Article analysis:

As an AI language model, I am not capable of providing a critical analysis of the article. However, I can provide some general observations about the content.

The article provides a comprehensive overview of prompt engineering for autoregressive language models, including basic prompting techniques such as zero-shot and few-shot learning, instruction prompting, self-consistency sampling, and chain-of-thought prompting. It also offers tips and resources for example selection and ordering.

One potential bias in the article is its focus on autoregressive language models only. While this is understandable given the author's expertise and interest, it may limit the scope of the discussion and overlook other types of language models that could benefit from prompt engineering.

Another potential bias is the author's personal opinion on some prompt engineering papers being too long. While this may be true for some papers, it is not necessarily a universal truth and could be seen as subjective.

The article does not provide much evidence or data to support its claims or recommendations. While it does reference several research papers and resources, it would benefit from more concrete examples or case studies to illustrate how prompt engineering can improve model performance.

Overall, the article provides a useful introduction to prompt engineering for autoregressive language models but could benefit from more balanced reporting and supporting evidence.