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# System prepended metadata

title: new-karpathy-post-20250625

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# new-karpathy-post-20250625

## “Context Engineering” > “Prompt Engineering”

Tobi Lütke recently coined (or at least popularized) a more accurate term for the core design challenge in building effective LLM applications:

> **Tobi Lütke** [@tobi] · Jun 18  
> *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.*

Andrej Karpathy followed up by unpacking what that *actually* looks like in practice inside real-world, industrial-strength LLM systems. Far from simply writing good prompts, Karpathy argues that **context engineering** is a sophisticated blend of art and science—one that requires architectural judgment, psychological intuition, and a growing stack of orchestration tooling:

> +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... Too little or of the wrong form and the LLM doesn't have the right context for optimal performance. Too much or too irrelevant and the LLM costs might go up and performance might come down. Doing this well is highly non-trivial. And art because of the guiding intuition around LLM psychology of people spirits.  
>  
> On top of context engineering itself, an LLM app has to:  
> - break up problems just right into control flows  
> - pack the context windows just right  
> - dispatch calls to LLMs of the right kind and capability  
> - handle generation-verification UIUX flows  
> - a lot more - guardrails, security, evals, parallelism, prefetching, ...  
>  
> So context engineering is just one small piece of an emerging thick layer of non-trivial software that coordinates individual LLM calls (and a lot more) into full LLM apps. The term "ChatGPT wrapper" is tired and really, really wrong.
