# What is Context Engineering, Anyway?

By Daniel Chalef · Jun 26, 2025 · https://www.getzep.com/blog/what-is-context-engineering/ · Tags: Context engineering, Agent memory, Context graphs

From Prompt Engineering to Context Engineering: The Why's and How.

Working with large language models reveals an interesting shift in how we talk about making these systems work well. The term "prompt engineering" dominated early discussions, but you'll increasingly hear engineers talking about "context engineering" instead. This isn't just semantic drift. It's a fundamental shift in how we think about deploying LLMs effectively.

> I really like the term “context engineering” over prompt engineering.
>
> It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.
>
> — tobi lutke (@tobi) ([post](https://twitter.com/tobi/status/1935533422589399127))

The change has gotten attention from notable industry figures. Tobi Lütke, CEO of Shopify, publicly endorsed the terminology on X (formerly Twitter), emphasizing that the core skill is "providing all the necessary context for the LLM" rather than just crafting clever prompts. Andrej Karpathy has also praised the term, describing context engineering as "the art of providing all the context for the task so that the LLM can solve it."

Context engineering, at its core, is the art and science of assembling all the necessary information, instructions, and tools around a large language model to help it accomplish a task reliably. Unlike basic prompt-tuning, which focuses on crafting clever wording for individual queries, context engineering involves building dynamic systems that feed an AI exactly what it needs, in the right format, to perform consistently across varied scenarios.

> +1 for "context engineering" over "prompt engineering".
>
> People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window…
>
> — Andrej Karpathy (@karpathy) ([post](https://twitter.com/karpathy/status/1937902205765607626))

## From Prompts to Context

The early days of working with LLMs like GPT-3 were all about "prompt archaeology": developers would spend hours tweaking the exact phrasing of their prompts, trying to coax better responses through clever wording tricks. Add "Let's think step by step" to get better reasoning. Prepend "You are an expert in..." to invoke domain knowledge. These techniques worked, but they were brittle and didn't scale well to complex applications.

As LLM-powered applications grew beyond simple question-answering demos, a fundamental limitation became obvious: the stateless nature of these models meant they only knew what you told them in each individual interaction. No matter how cleverly you worded your prompt, if the model lacked essential context about the user, the task, or the domain, it would struggle to provide useful responses.

This insight changed how people approached the problem. Harrison Chase defines context engineering as "building dynamic systems to provide the right information and tools in the right format such that the LLM can plausibly accomplish the task." The emphasis here is on *systems*: not just individual prompts, but entire pipelines that gather, format, and supply relevant context to models at runtime.

> 📃The rise of context engineering
>
> "Context engineering" has been an increasingly popular term used to describe a lot of the system building that AI engineers do
>
> But what is it exactly?
>
> The definition I like:
>
> "Context engineering is building dynamic systems to provide the…
>
> — Harrison Chase (@hwchase17) ([post](https://twitter.com/hwchase17/status/1937194145074020798))