Context Engineering
Context Engineering is a technical discipline focused on systematically designing and optimizing the context provided to AI models — including codebase structure, commit history, design intent, and domain knowledge.
If prompt engineering is the craft of "how to write a single question well," context engineering sits one layer above it. It is the work of designing "what to show the AI, in what order, and how much." Anthropic's 2026 report introduces a concept called "Repository Intelligence" — the ability of an AI agent to work with an understanding of the relationships and intent of an entire repository, rather than at the level of individual lines of code. Achieving this makes the quality and structure of the context passed to the agent critically important. Concretely, this involves design decisions such as the following: - Which files to include in the context, and which to exclude - How to communicate project rules (coding conventions, architectural policies) - How to narrow down past change history to the portions relevant to the current task - How to compress information to maximize its density within the constraints of the context window Claude Code's CLAUDE.md and Rules files, and OpenClaw's long-term memory feature, are all examples of context engineering in practice — mechanisms by which developers structure project-specific knowledge and pass it to the AI. The phase of refining how prompts are written is over. We have entered the phase of designing "the environment itself in which the AI works."
Related Terms
Ambient AI
Ambient AI refers to an AI system that is seamlessly embedded in the user's environment, continuously monitoring sensor data and events to proactively take action without requiring explicit instructions.
Claude Code
Claude Code is a terminal-resident AI coding agent developed by Anthropic. It is a CLI tool that enables users to consistently perform codebase comprehension, editing, test execution, and Git operations through natural language instructions.
LoRA
LoRA (Low-Rank Adaptation) is a technique that inserts low-rank delta matrices into the weight matrices of large language models and trains only those deltas, enabling fine-tuning by adding approximately 0.1–1% of the total model parameters.
Let's discuss your needs
Whether it's AI adoption, process improvement, or cost reduction — we're here to help. With 1,850+ client engagements, we'll find the right solution for you.
Free consultation. We'll respond within 24 hours.