Context Engineering

Context Engineering is the discipline of designing and building dynamic systems that provides the right information and tools, in the right format, at the right time, to give a LLM everything it needs to accomplish a task.

• A System, Not a String: Context isn't just a static prompt template. It’s the output of a system that runs before the main LLM call.
• Dynamic: Created on the fly, tailored to the immediate task. For one request this could be the calendar data for another the emails or a web search.
• About the right information, tools at the right time: The core job is to ensure the model isn’t missing crucial details ("Garbage In, Garbage Out"). This means providing both knowledge (information) and capabilities (tools) only when required and helpful.
• where the format matters: How you present information matters. A concise summary is better than a raw data dump. A clear tool schema is better than a vague instruction.

Context engineering is the practice of designing systems that decide what information an AI model sees before it generates a response.

Even though the term is new, the principles behind context engineering have existed for quite a while. This new abstraction allows us to reason about the most and ever-present issue of designing the information flow that goes in and out of AI systems.

Instead of writing perfect prompts for individual requests, you create systems that gather relevant details from multiple sources and organize them within the model’s context window. This means your system pulls together conversation history, user data, external documents, and available tools, then formats them so the model can work with them.

• The prompt and instructions you give to the model
• Any documents or external data you retrieve (e.g. RAG)
• Any past state, tool calls, results, or other history
• Any past messages or events from related but separate histories/conversations (Memory)
• Instructions about what sorts of structured data to output

Unlike prompt engineering, which focuses mainly on crafting clever instructions for LLMs, context engineering is the systematic discipline of designing and optimizing the surrounding environment in which AI systems operate. It goes beyond prompts to carefully structure the data, tools, information and workflows that maintain the overall context for an AI system. By doing so, context engineering ensures that tasks are executed not just creatively, but reliably, consistently and intelligently.

At its core, context engineering acknowledges that an LLM by itself knows nothing relevant about a task. Its effectiveness depends on the quality and completeness of the context it receives. This involves curating the right knowledge sources, integrating external systems, maintaining memory across interactions, and aligning tools so the AI agent always has access to what it needs, when it needs it. Small gaps in context can lead to drastically different outcomes — errors, contradictions or hallucinations.

That’s why context engineering is emerging as one of the most critical practices in building robust AI applications. It’s not just about telling the model what to do; it’s about setting up the stage, the rules and the resources so the AI can make better decisions, reason effectively and adapt to real-world complexity.

Context engineering is this emerging field where you curate what the model sees so that you get a better result. That is what context engineering is.

Gartner defines context engineering as designing and structuring the relevant data, workflows and environment so AI systems can understand intent, make better decisions and deliver contextual, enterprise-aligned outcomes — without relying on manual prompts.