Context Engineering
In one line
Context engineering goes beyond crafting a single prompt — it is the design discipline of deciding which context to assemble and how to feed it to the model, an idea that crystallised in 2024–2025.
Going deeper
Context engineering is the natural evolution of prompt engineering. It covers everything that ends up inside the context window — system prompt, few-shot examples, RAG-retrieved documents, tool outputs, conversation summaries — and how those pieces are assembled, ordered and compressed. As models get stronger, the assembly increasingly outweighs any single clever line of prompting.
Two angles matter for marketers. First, the quality of any in-house AI tool or agent ends up being a function of how well its context is engineered. Second, your content has to behave well when an AI pulls it into context — which means clean chunks, clear definitions and structured metadata. Those same habits are what make GEO work.
In practice, teams now treat system prompt, retrieved snippets, tool responses, user message order and compression as design choices. The era of 'just write a better prompt' is fading, and diagnosing a bad AI output usually starts with the context design rather than the model itself.
Related terms
Prompt Engineering
Prompt engineering is the practice of crafting inputs that steer an LLM toward better outputs — a way to dramatically change result quality without retraining the model.
LLMSystem Prompt
A system prompt is the instruction sent to an LLM before any user message, defining the assistant's role, tone and rules — effectively the AI product's character.
LLMRAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
LLMContext Window
The context window is the maximum number of tokens an LLM can take in at once — it defines how much content the model can consider in a single prompt.
LLMContext Rot
Context rot is the phenomenon where LLM accuracy degrades as the context grows longer — the empirical reason a giant context window does not automatically mean better answers.
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