Context Engineering: Buzzword or real revolution?
In the tech world, every so often a new label appears that changes everything… at least on LinkedIn. And lately, the term on everyone’s lips is context engineering. Is it a simple evolution of prompt engineering? A passing fad? Or the key for artificial intelligence to stop looking like a clumsy chatbot and become a truly useful agent?
Spoiler: it’s the latter. But let’s take it step by step.
The hype: more than a pretty prompt
During 2023, the term prompt engineering reached its peak. Everyone was talking about how to write magic prompts that brought out the best in ChatGPT. But as technology advanced, it became clear that writing a good prompt was not enough.
Here comes the real protagonist: the context.
Generative artificial intelligence (especially LLMs) has no real memory nor does it understand the world. It only responds based on the context we give it. And that context, well-designed, is what differentiates a generic bot from an intelligent, personalized agent with common sense.
That’s where context engineering comes into play.
What is Context Engineering
Context engineering is the discipline of designing and maintaining dynamic systems that organize and deliver all the necessary information—at the right time and in the right format—to the language model so it can complete a task effectively.
It’s not just about improving a prompt. It’s about building a complete context architecture, capable of selecting, prioritizing, compressing, validating, and delivering exactly what the model needs to act with precision and coherence.
How is it different from prompt engineering?
Let’s clarify it with a direct comparison:
Prompt Engineering: consists of carefully formulating a prompt for a specific task. It’s a valuable skill, yes, but limited to static and simple scenarios.
Context Engineering: is building a complete system that dynamically handles what information reaches the model, when, and how. It involves architecture, memory, tool management, noise filtering, validation, information retrieval, compression… in short, everything necessary so that the model doesn’t work blindly.
In other words: if prompt engineering is about writing a good question to the model, context engineering is about making sure the model has access to all possible answers before we even ask.
Key components of context
Context Selection
Not all information is useful. Part of the challenge is knowing what data to include: is it necessary to remember the full conversation history? Is it useful to load external documents? Should it have access to the user’s calendar? Context engineering seeks the balance between relevance and economy.
RAG and Memory
This is where short-term and long-term memory come in, in addition to retrieval-augmented generation (RAG) techniques. The system must be able to retrieve, summarize, and organize historical user information or external sources intelligently.
Order and Compression
Models have a token limit, so we can’t give them “all the context in the world.” The key lies in compressing, summarizing, and prioritizing information. Sometimes the most recent information matters, other times the most important. And, by the way, the order does alter the result.
Error Prevention (Context Poisoning)
As conversational agents become more complex, one must prevent erroneous, biased, or contradictory information from infiltrating the context. This involves pruning, cross-validation, or even temporal isolation mechanisms for certain sources.
Tool Use and Workflows
Context engineering is not limited to text. It must also integrate external tools, such as calendars, search engines, APIs, or internal systems. And most importantly: it must design coherent workflows where the model knows when and how to use each tool. This is also starting to be called workflow engineering.
Why it matters (and it’s not just a buzzword)
If models like GPT are already powerful, why complicate our lives with this?
Because without a well-designed context, the model can only generate generic, impersonal, and error-prone responses. With good context, on the other hand, it can:
- Maintain coherence in a prolonged conversation
- Adapt to the user’s style, history, and preferences
- Integrate real-time data
- Avoid hallucinations
- Make decisions based on verifiable information
In a business environment, the competitive advantage is not in the model you use, but in how you feed it. Furthermore, the integration of agents in development environments, as seen in Microsoft Build 2025, reveals how a solid contextual architecture allows tools like GitHub Copilot to move from assistant to autonomous agent.
Are we all on the context engineering bandwagon?
Yes, there is hype. And yes, there are those who are using it as a marketing keyword to sell smoke. But there is also a very solid and very real technical background. Context is the new prompt. And whoever masters its engineering will be the one creating the AI agents that truly make a difference.