In recent years, the development and integration of language models such as Chatgpt, Claude or Gemini have deeply modified the way we interact with technology. One of the most demanded skills in this new paradigm has been the art of writing prompts: well -formulated instructions for AI systems to generate useful, consistent and relevant responses.
However, as the models become more complex and capable, this ability has evolved. Today, what makes the difference is not only what is asked of AI, but how and with what context the complete interaction is formulated. It is here where an emerging discipline comes into play: context engineering.
“With the sophistication and increase in the capabilities of the LLM, the Prompts engineering has evolved to context engineering, which takes into account new parameters to improve the responses of the chatbots, adjusting them to what the user needs,” explains Marcelo Pacheco, director of the Systems Engineering career of the Franz Tamayo University, Unifranz.
But what exactly is this context engineering and why is it winning so much relevance? Unlike the classic prompt – a brief phrase or set of instructions that seeks to guide the response of a language model – context engineering designs and structures the entire informative environment in which a task is framed, offering the AI much more than a simple order.
Beyond the Prompt: What does the “context” understand?
The context, in the field of artificial intelligence, is all that the model sees before generating an answer. It is not only the user’s question, but also a broader set of elements that directly affect system performance. Among them are:
- System instructions: initial configurations that define the behavior of the model.
- Conversation history: previous interactions, both of the user and the system.
- Long -term memory: accumulated and personalized knowledge of the user or environment.
- External information (known as RAG, Retrieval-Augmented Generation): Documents, APIs, Databases or files connected to the model.
- Available tools: functionalities or automated actions that the system can execute.
- Expected output format: Structures defined as JSON responses, pictures or lists.
In order for an AI to work precisely, all these elements must be designed, selected and organized carefully. That is the role of context engineering: act as an intelligent interface between the model and its operational environment, guaranteeing that you receive the appropriate data at the right time, in the correct format and with the relevant resources.
One of the clearest analogies on the impact of the context was recently presented in the technical community: imagine that an AI receives a message like “Are you available tomorrow for a rapid synchronization?” If IA only has access to this message (without additional information), it will respond with something vague and little useful. On the other hand, if the model has access to the user’s agenda, to previous Correos, to the usual tone of conversation and tools such as the calendar, you can answer with a message of the type: “Hello Jim, tomorrow I am full. Thursday in the morning it serves you? I already sent you the invitation.”
The difference between both scenarios is not in the intelligence of the model, but in the quality of the context provided. And therefore, building that context in a structured way becomes the key ability of the present.
Components and dynamics of context engineering
According to Pacheco, this discipline is based on writing instructions not only in the form of questions, but on carefully designed fragments that guide AI models to generate more precise, human and useful answers. Some of the most important elements in this engineering are:
- Dynamic design: Each task may require different data. For a conversation, it will suffice with history; For another, you may require access to external databases or databases.
- Selection of relevant information: it is not about saturating the model with data, but about delivering exactly what you need to fulfill your task.
- Adequate format: the way in which the information is presented influences the result. Concisous summaries, tables, or even well -structured simulations are more effective than extensive text blocks.
- Tool management: integrate functions that the model can execute (such as sending an email or generating an alert) substantially improves its usefulness.
Challenges and future
Although Prompts engineering remains a useful tool, its limitations are evident when working with more sophisticated models. “There are still weaknesses in the final machine-user conversations, with difficulties in the responses that are generated,” says Pacheco. That is why today it insists on the need to train not only programmers, but multidisciplinary teams capable of designing rich, relevant and well structured contexts.
In this sense, context engineering is not simply a technical skill, but a transverse discipline that combines programming knowledge, UX, data processing, and communication. Its generalized adoption can make the difference between a digital product that barely works and another that really transforms the user experience.
The advance of artificial intelligence raises a new challenge: it is not enough to have larger or sophisticated models, but we need to feed them with the appropriate data, presented in the right way and at the right time. That is the essence of context engineering.
