Context Engineering: The next big AI skill?

What is Context Engineering?

Context engineering is about giving AI the full environment it needs to solve a problem, rather than relying on a single prompt. Shopify CEO Tobi Lütke describes it as “the art of providing all the context for the task to be plausibly solvable by the LLM”. In practice, this means designing systems that feed an AI model the right information and tools at the right time. Phil Schmid puts it this way: context engineering is the discipline of designing and building dynamic systems that “provide the right information and tools, in the right format, at the right time”.

From Prompts to Context Systems

Prompt engineering focused on writing the perfect question or instruction. Context engineering takes a broader view: it is about assembling the whole information environment around the AI. For example, an AI agent might draw on memory, documents, databases, and user history together – not just one static prompt. In other words, instead of a single text prompt, context engineering means dynamically gathering all the needed background for each request. Schmid emphasizes that prompt engineering is about a single set of instructions, whereas context engineering is about building an entire system that dynamically tailors context to each task.

Key Trends Driving the Shift

• Massive Context Windows: New AI models can handle far more text at once. Google’s Gemini 1.5 Pro, for example, can use up to 1 million tokens of context. By contrast, older models like GPT-3 only took around 2,048 tokens. This expansion means AI can read entire documents or codebases in one go, so we can feed it much richer information.
• Agent-based AI: AI is moving from one-shot chat to agents that perform tasks over many steps. In this setting, context is everything. AI developer Philipp Schmid notes that the difference between a cheap demo and a “magical” agent is the quality of the context provided – he says most agent failures are context failures, not model failures. In short, agents often fail when they lack key information.
• Memory and State: Context engineering lets AI systems remember and build on past interactions. For example, techniques like “scratchpad” memory let an agent save useful information as it works, and models can pull in those memories later. Many systems (for instance, features in ChatGPT and other agents) keep user preferences or past conversation highlights in memory. This means the AI can maintain state and improve answers over time instead of “forgetting” everything after each session.

Components of Context Engineering

A context engineering system weaves together many elements, such as:

• System Instructions: The base rules or guidelines (sometimes called a system prompt) that define how the AI should behave.
• User Input: The immediate question or command from the user.
• Short-term Memory: The recent conversation history in this session, so the AI knows what’s already been said.
• Long-term Memory: Facts or knowledge carried over from past tasks or stored in databases (like summaries of previous sessions or user preferences).
• Retrieved Information: Any external data fetched on demand (via search, knowledge bases, or APIs) to help answer the current query.
• Available Tools: Definitions of functions or plugins the AI can use (for example, a “send_email” or “search” tool).
• Structured Output: Rules for how the AI’s answer should be formatted (such as requiring a JSON object with specific fields).

Context engineering treats all these pieces as parts of one engineered system. It’s an architectural approach: you decide when to pull in memory, when to query a database, and how to format everything so the model has exactly what it needs.

Why It’s Being Called the Next AI Skill

Many experts now call context engineering a must-have skill. For example, Andrej Karpathy (former head of Tesla AI) calls it the “delicate art and science of filling the context window with just the right information for the next step”. LangChain founder Harrison Chase likewise says it is “the most important skill an AI engineer can develop”.

Companies are taking notice. Harper, a startup building AI insurance tools, explicitly advertised an “AI Context Engineer” role to build the data pipelines that feed contextual data into their models. Framework makers are also supporting this shift: for example, the LangChain LangGraph toolkit is designed to give developers full control of every step of context construction.

In practice, teams have reported big gains from richer context (some claim accuracy boosts and fewer project failures, though formal studies are still scarce). The idea is straightforward: when the AI has more relevant context, it often does a better job. Even without exact numbers, the trend is clear: a well-architected context can make a standard model perform like a much smarter one.

Who Can Learn Context Engineering?

Lower barriers: The basic idea is intuitive: give the AI everything it needs. Many users already do parts of this. For example, using a document search or a vector database to answer questions (a common “RAG” setup) is already a form of context engineering. Like prompt engineering, it can be learned by experimenting – try adding different info sources or memory and see what improves the answers.

Higher barriers: Building a full context system can be technical. It may require setting up databases, writing search or ETL pipelines, and integrating APIs. For example, Harper’s job description calls for expertise in scalable data pipelines and vector databases (it specifically mentions Qdrant). It also requires domain expertise: building a good context means knowing what information matters in that field (for example, patient records in healthcare or policy details in insurance).

Skills for Context Engineers

Context engineering draws on several skills:

• Technical: Building retrieval and data pipelines. For example, you might use search engines, vector stores, or APIs to gather information. Harper’s listing even mentions tools like Apache Airflow and distributed systems for moving data.
• Data Architecture: Designing where and how information (memories, documents, user data) is stored and accessed. You need to structure knowledge so the AI can retrieve it efficiently.
• Analytical: Synthesizing information from many sources. Good context often combines insights from different fields, so being able to connect ideas across domains is valuable.
• Systems Thinking: Planning how all pieces fit together over a workflow. LangChain points out that context engineering is effectively the #1 job when building AI agents. You must anticipate how the agent will use each bit of context throughout its multi-step task.
• User Understanding: Designing context to serve the end user. For instance, deciding which user details to keep in memory and how to frame prompts so the AI’s responses feel natural and helpful.

The Interdisciplinary Edge

The best context often comes from combining different kinds of knowledge. LangChain notes that context can come from the developer, the user, past interactions, tool outputs, and more. This means blending technical know-how with domain insight and human factors. People who think across boundaries – mixing business strategy, domain expertise, and data – tend to create richer context. In practice, an effective context engineer might draw on psychology to set the tone, on UX design to structure a conversation, and on data skills to fetch relevant facts.

The Future of Context Engineering

Context engineering is already shaping AI’s future, and several trends are emerging:

• Specialized Roles: Just as data engineers became common, “context engineer” may become a standard role. Startups like Harper are already hiring for it, and we expect more companies to follow.
• Better Tools: New frameworks and platforms will make context engineering easier. Agent toolkits like LangGraph give developers explicit control over how context is assembled, and other libraries (e.g. LlamaIndex, AI chat platforms) are adding memory and retrieval features.
• Strategic Priority: Organizations are realizing that AI success depends on data strategy. The competitive edge will go to teams that design data flows as carefully as code. In other words, context engineering is moving from a technical detail to a business strategy: the systems that best organize and deliver information to an AI will produce the most reliable and innovative solutions.

Conclusion: Thinking with AI

Context engineering represents a shift from simply “talking to AI” to truly partnering with it. Instead of trying to guess a perfect prompt, we build systems so the AI can reason with everything it needs on hand. This requires new skills – in data, architecture, and interdisciplinary thinking – but it also opens big opportunities. As AI models get smarter and can handle longer context, the organizations that win will be those who architect context as carefully as they build the code.