yazzy

The term context engineering has recently started to gain traction as a better alternative to prompt engineering. I like it. I think this one may have sticking power.

Here's an example tweet from Shopify CEO Tobi Lutke:

I really like the term “context engineering” over prompt engineering.

It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.

Recently amplified by Andrej Karpathy:

+1 for "context engineering" over "prompt engineering".

People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step. Science because doing this right involves task descriptions and explanations, few shot examples, RAG, related (possibly multimodal) data, tools, state and history, compacting [...] Doing this well is highly non-trivial. And art because of the guiding intuition around LLM psychology of people spirits. [...]

I've spoken favorably of prompt engineering in the past - I hoped that term could capture the inherent complexity of constructing reliable prompts. Unfortunately, most people's inferred definition is that it's a laughably pretentious term for typing things into a chatbot!

It turns out that inferred definitions are the ones that stick. I think the inferred definition of "context engineering" is likely to be much closer to the intended meaning.