Editor's Synopsis
François Chollet shares his interpretation of prompt engineering in this post on X. He frames a large language model (LLM) as a repository of millions of vector programs that have been implicitly mined from human-generated data as a by-product of language compression. He defines a "vector program" as a highly non-linear function that maps part of an input space to outputs.
The post appears to be truncated, but the core insight Chollet presents is that prompt engineering is essentially the process of selecting and activating specific vector programs from the vast collection stored within an LLM. This perspective reframes LLMs not as reasoning engines but as compressed repositories of learned patterns, offering a more mechanistic view of how prompts interact with model internals.