What is Context Engineering?
The Rise of Context Engineering: Beyond Prompt Engineering
Over the past few years, prompt engineering has been the dominant skill when working with large language models (LLMs). Developers learned how to craft clever prompts, chain instructions, and fine-tune interactions. But as AI systems evolve into more complex, agentic workflows, a new discipline is emerging—context engineering.
What is Context Engineering?
At its core, context engineering is the practice of managing what an AI model sees and when it sees it. Instead of focusing only on phrasing the right question, context engineering is about structuring, filtering, and delivering the right information into the model’s context window.
As LLMs grow more powerful, their weaknesses often come down to missing or mismanaged context—leading to hallucinations, irrelevant answers, or broken reasoning. By contrast, strong context engineering enables models to stay grounded, recall long conversations, and perform complex reasoning across tasks.
Why It Matters
Traditional prompt engineering works well for simple, one-off queries. But when building AI systems that:
- maintain long-term memory,
- reason across multiple steps,
- interact with tools and APIs,
- or collaborate as multi-agent systems—
then prompts alone are not enough. What matters most is how context is managed.
These techniques help ensure that the model isn’t overloaded with noise, but instead has exactly the right information at the right time.
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