Context engineering is the deliberate design of input context to guide LLM behavior and output quality. It involves structuring prompts, examples, and background information to help the model understand task requirements and generate accurate, relevant responses.

Key characteristics:

• Context window management - Organizing information within token limits for maximum impact
• Information hierarchy - Placing critical details where the model weighs them most heavily
• Example selection - Choosing representative few-shot examples that guide desired output patterns
• Constraint definition - Explicitly stating boundaries, formats, and requirements

Example: Instead of “Explain database indexing,” use structured context: “You are a senior database engineer. Explain database indexing to a junior developer who understands SQL basics but hasn’t worked with performance optimization. Use a concrete example with a users table.”

This approach transforms generic responses into targeted, contextually appropriate outputs.