Context Management: Definition and Examples

Context management is a fundamental discipline of prompt engineering that involves mastering the background information provided to a large language model (LLM). Each model has a limited context window — measured in tokens — within which instructions, reference data, and conversation history must fit. Effectively managing this context ensures that every token used brings maximum value to the response.

In practice, context management covers several operations: selecting relevant information to include in the prompt, prioritizing it (from most important to least critical), compressing or summarizing previous exchanges, and dynamically injecting external data through techniques like RAG (Retrieval-Augmented Generation). The goal is to provide the model with exactly what it needs to respond, no more and no less.

Poor context management leads to concrete problems: the model "forgets" instructions given at the beginning of the conversation, produces responses inconsistent with previous exchanges, or becomes saturated with irrelevant information that dilutes response quality. Conversely, rigorous context management allows for maintaining long, coherent conversations, fully exploiting the model's capabilities, and reducing costs associated with using unnecessary tokens.

Context management has become even more strategic with the emergence of autonomous AI agents and complex multi-turn systems. In these architectures, context must be managed programmatically: persistent memories, automatic summarization systems, instruction files (like CLAUDE.md files), and dynamic prioritization mechanisms are all tools for effective context management.