Inference
Context Engineering
The discipline of designing what information goes into a model's context window and how it is structured.
In depth
The discipline of designing what information goes into a model's context window and how it is structured. Context engineering goes beyond prompt engineering by managing system prompts, retrieved documents, tool results, conversation history, and memory to give the model exactly the right information at the right time. It is the difference between a model that kind of works and one that works reliably.
Example
In practice, developers reach for Context Engineering when they need the capability described above as part of an AI feature or workflow.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Context Engineering?
The discipline of designing what information goes into a model's context window and how it is structured.
Why does Context Engineering matter for AI developers?
Context Engineering sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Context Engineering?
Developers Digest publishes tutorials and videos that cover Inference topics including Context Engineering. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
In-Context Learning
The ability of a language model to learn new tasks from examples or instructions provided in the prompt, without any weight updates or training.
Inference
Context Rot
The decline in LLM output quality as the number of input tokens grows, even on simple tasks that fit well inside the context window.
Inference
Continued Pretraining
Running more next-token-prediction training on an already pretrained model using a large unlabeled corpus from a target domain, such as code, math, medicine, or a new language.
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