Context Engineering in AI: Enhancing Model Performance

Introduction

Context engineering represents a systematic approach to optimizing the information provided to artificial intelligence (AI) models, particularly large language models (LLMs) and AI agents. It emphasizes that effective prompts constitute only a minor portion of success, while comprehensive context—encompassing instructions, examples, knowledge, memory, tools, and the overall context window—drives the majority of outcomes. This methodology ensures AI systems receive relevant data dynamically, enabling more accurate and reliable responses. As of September 2025, context engineering remains highly relevant, evolving from prompt engineering to address complex, real-world AI deployments in agentic systems.

Functionality and Capabilities

Context engineering functions by curating and structuring information flows into an AI model's input. It determines what data the model accesses prior to generating outputs, incorporating elements such as detailed instructions for behavior, few-shot examples to demonstrate patterns, domain-specific knowledge from external sources, short- and long-term memory for continuity, tool integrations for extended capabilities, and management of the context window to avoid overload. This enables AI to handle multifaceted tasks, such as reasoning over dynamic data or executing multi-step processes, far beyond static prompting. For instance, in agentic systems, it facilitates tool selection and memory retrieval to accomplish goals reliably.

Use Cases Across Industries