Context Engineering
Published on: October 07, 2025
Prompt Engineering vs. Context Engineering
graph TD subgraph "Context Engineering" direction TB E[User Input] --> F[Orchestrator]; F --> G[Retrieve Knowledge]; F --> H[Access Memory]; F --> I[Select Tools]; G --> J[Dynamic Context]; H --> J; I --> J; J --> K{LLM}; K --> L[Output]; L --> F; end subgraph "Prompt Engineering" direction TB A[User Input] --> B[Crafted Prompt]; B --> C{LLM}; C --> D[Output]; end
Core Components of the LLM's Context
mindmap root((LLM Context)) ::icon(fa fa-brain) System Instructions (Persona, Role, Constraints) User Input (Current Query) Memory Systems Short-Term Memory Long-Term Memory Retrieved Knowledge (RAG, APIs, Databases) Tool Schemas (Function Descriptions) Structured Outputs (JSON, XML, etc.)
Retrieval-Augmented Generation (RAG) Flow
graph TB A[User Query] --> B{Retrieval}; C[External Knowledge Base] --> B; B --> D[Relevant Documents]; D --> E{Augmentation}; E --> F[Generated Response];
Advanced Context Engineering Workflow
graph TD A[User Input] --> B{Orchestrator}; B --> C{Need external knowledge?}; C -- Yes --> D[RAG: Retrieve & Augment]; C -- No --> E[Process Input]; D --> E; E --> F{Need a tool?}; F -- Yes --> G[Select & Execute Tool]; G --> H[Get Tool Output]; H --> I; F -- No --> I[Manage Context Window]; I --> J[Access Memory]; J --> K[Construct Final Prompt]; K --> L(LLM Call); L --> M[Parse Output]; M --> N[Update Memory]; N --> O[Final Response];