Definition
What is Prompt Engineering?
Last updated
The practice of designing the text of prompts sent to a language model to improve the quality, format, or reliability of its output.
Prompt engineering focuses on how instructions are phrased. Context engineering, by contrast, focuses on what information the model has access to. For production AI systems, the bottleneck is almost always context, not phrasing. Prompt engineering remains useful for output shaping and tool-use scaffolding, but it cannot compensate for missing or stale data.
Further reading
Articles about Prompt Engineering
Agentic context engineering: how ACE evolves contexts
ACE (ICLR 2026) beats tuned prompts by 10.6% with self-evolving contexts that avoid brevity bias and context collapse, two real failures of prompt tuning.
TOON vs JSON: why smaller doesn't mean cheaper for LLMs
TOON looks more compact than JSON, but a 9,649-test study found it cost LLMs 38% more tokens. The reason: model training distribution beats format size.
Context engineering: the end of prompt engineering
Prompt engineering is a dead end. Context engineering — designing what information AI models receive — is replacing it. Here's how to start applying it.
Context engineering: what replaces prompt engineering
Prompt engineering has a new successor: context engineering. Learn why Karpathy and Tobi Lütke made the switch, and what it means for production AI systems.
All terms
View full glossaryPut context into practice
Create your first context container and connect it to your AI tools in minutes.
Create Your First Container