context-engineering-basics

What problem does it solve?

Context engineering basics provide a structured approach to optimize how AI systems discover, compress, cache, and load contextual information. It enables agents to operate with limited tokens while maintaining high-quality, context-aware responses across long conversations and multi-CLI environments.

Core Features & Use Cases

• Dynamic Context Discovery: Identify and load only the most relevant knowledge segments at runtime.
• Context Compression: Summarize and prune historical and retrieved content to maximize token efficiency.
• Memory Consolidation: Organize data into working, episodic, and semantic memory for continuity across sessions.
• JIT Loading: Incrementally load context on demand to balance accuracy and token usage.
• Use Case: Build a chat assistant that maintains long-term context over many turns while staying within token limits and adapting to new tasks.

Quick Start

Start by outlining how to apply context-engineering-basics to a multi-turn dialogue, load essential context first, then progressively expand with relevant references as the conversation evolves.