Prompt Engineering vs Context Engineering

In the world of AI and Large Language Models (LLMs), how you interact with the model determines the quality of the output. Two key concepts—Prompt Engineering and Context Engineering—are essential to effectively use these models. Although they sound similar, they serve different purposes and strategies. Understanding these concepts can help you get better results from AI models like ChatGPT, Claude, or Gemini.

What is Prompt Engineering?

Prompt Engineering is the art and science of crafting effective instructions (prompts) to get the desired response from an AI model. It involves structuring questions, commands, or templates in a way that the model understands your intent clearly. For example, asking “Summarize the following text in 3 bullet points” instead of just pasting the text will usually lead to a better response.

Prompt Engineering focuses on how to phrase the input and what specific instructions to give. It is especially useful for short, task-based interactions where clarity, tone, and specificity matter.

 

What is Context Engineering?

Context Engineering is about designing and managing the background information or setup that an AI model uses to generate a response. While Prompt Engineering focuses on the “what to ask,” Context Engineering focuses on the “what the model should know while answering.”

This could include injecting prior conversation history, relevant documents, role-based behavior, or domain-specific guidelines into the model’s input so it produces consistent and accurate outputs over long or complex sessions.

 

Example of Context Engineering

Suppose you are building an AI legal assistant. Instead of asking the AI to answer questions from scratch every time, you feed it relevant legal policies, the user’s previous questions, and their preferences. This allows the model to give more personalized and legally consistent answers.

For example, you can preload the model with: “You are an AI legal assistant specializing in labor law in India. The user prefers brief, actionable advice. Use Indian legal terms where applicable.” — This is a form of Context Engineering that defines the AI’s behavior and memory scope.

Differences between Prompt Engineering and Context Engineering

	Prompt Engineering	Context Engineering
Focus	Crafting the question or instruction	Managing the background information and model behavior
Scope	Single task or response	Ongoing session or multi-turn conversation
Goal	Get accurate and relevant output	Maintain consistency and personalization
Usage	One-off queries or commands	Complex applications like chatbots, assistants, or workflows
Example	“Translate this paragraph into French”	“User is a French learner, respond with translations and explanations”