Context Fundamentals

Context Fundamentals teaches AI engineers to understand and optimize how language models use available information during inference. This foundational skill is essential for anyone designing agent systems, debugging context-related issues, or optimizing token usage for cost efficiency.

At its core, context is the complete state available to a language model at inference time—encompassing system instructions, tool definitions, retrieved documents, conversation history, and tool outputs. Understanding these components and their interactions is critical for building effective AI agents. The skill defines the fundamental constraint that models face: an attention budget where computational relationships scale quadratically with token quantity, leading to degradation in long-range reasoning even when models technically support longer context windows.

Key concepts covered include System Prompts (establishing agent identity and behavioral guidelines with the right balance of specificity and flexibility), Tool Definitions (specifying available actions with descriptions, parameters, and usage examples), Retrieved Documents (just-in-time domain knowledge following patterns similar to human information retrieval), Message History (tracking conversation progress across turns), and Tool Outputs (which often dominate context usage at up to 83.9 percent in typical interactions).

The central engineering principle states that context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes. Larger context windows do not automatically solve problems—careful curation matters more than volume. The skill provides strategies for selective retention, context compaction, and prioritizing high-signal information.

Developers building AI agents should master context fundamentals to create more reliable, efficient, and cost-effective systems. Whether designing RAG pipelines, multi-turn conversational agents, or complex tool-using systems, understanding context constraints and optimization strategies is essential for production-quality AI applications.