Context engineering is the systematic design and optimization of the information provided to a large language model during inference to reliably produce the desired output. It involves structuring, selecting and sequencing contextual elements — such as prompts, retrieved data, memory, instructions and environmental signals — so the model’s internal layers operate in an optimal state. Unlike prompt engineering, which focuses only on the wording of prompts, context engineering considers the entire configuration of context: how relevant knowledge, instructions and prior context are organized and delivered to achieve the most effective results.
Today, engineers use a range of discrete techniques that can be grouped into three areas: Context setup covers curation tactics such as using minimal system prompts, canonical few-shot examples and token-efficient tools for decisive action. Context management for long-horizon tasks addresses finite context windows through context summarization , structured note-taking to persist external memories and sub-agent architectures to isolate and summarize complex sub-tasks. Dynamic information retrieval relies on just-in-time (JIT) context retrieval, where agents autonomously load external data only when immediately relevant, maximizing efficiency and precision.
