inappropriately change or overwrite JSON files compared to Markdown files

improved with grading criteria that encode design principles and preferences.

But the researchers quickly realized that a model’s complexity wasn’t the only driving factor. Some unexpected abilities could be coaxed out of smaller models with fewer parameters — or trained on smaller data sets — if the data was of sufficiently high quality. In addition, how a query was worded influenced the accuracy of the model’s response.

prompt engineer. His role involves creating and refining the text prompts people type into the AI in hopes of coaxing from it the optimal result. Unlike traditional coders, prompt engineers program in prose, sending commands written in plain text to the AI systems, which then do the actual work.

Including a prompt prefix in the chain-of-thought style encourages the model to generatefollow-on sequences in the same style, which isto say comprising a series of explicit reasoningsteps that lead to the final answer. This abilityto learn a general pattern from a few examples ina prompt prefix, and to complete sequences in away that conforms to that pattern, is sometimescalled in-context learning or few-shot prompt-ing. Chain-of-thought prompting showcases thisemergent property of large language model at itsmost striking.

Dialogue is just one application of LLMs thatcan be facilitated by the judicious use of promptprefixes. In a similar way, LLMs can be adaptedto perform numerous tasks without further train-ing (Brown et al., 2020). This has led to a wholenew category of AI research, namely prompt en-gineering, which will remain relevant until wehave better models of the relationship betweenwhat we say and what we want.

In the background, the LLM is invisiblyprompted with a prefix along the following lines.