Context Engineering vs. Prompt Engineering: The Definitive Guide

Simply put, prompt engineering is giving precise instructions or crafting the perfect question so the LLM can get the right answer, whereas Context Engineering is giving the right data, information, and metadata so the AI can answer your question accurately.

Here are some quick guides:

1. LangChain’s guide to context engineering
2. Context Engineering Guide

Large Language Models (LLMs) are rapidly growing in capability. Recent months have seen major advancements: models now handle significantly larger context windows (e.g., Magic.dev’s LTM-2-Mini at 100 million tokens, Meta’s Llama 4 Scout at 10 million tokens, and offerings from OpenAI, Google, and Anthropic reaching 1 million tokens or more).

We also have highly capable smaller models like Microsoft’s Phi-3 series and Google’s Gemma that run efficiently on edge devices. Plus, LLMs are getting better at multimodal understanding and complex reasoning abilities. Because of these rapid improvements, how we interact with LLMs is constantly changing. While “prompt engineering” – crafting the perfect question to get a desired response – has been the primary focus, a new and broader idea is gaining significant traction: Context Engineering.

A few weeks ago, Tobi Lütke, CEO of Shopify, started a conversation on X about context engineering is different than prompt engineering from the perspective of giving enough context to solve a problem.

Andrej Karpathy, known for coining “Vibe Coding” and his foundational work on Tesla’s Autopilot AI and great video explainer on LLMs, joined the debate. So did Amjad Masad, the CEO of Replit.

Let’s explore the differences between these two approaches, why context engineering is becoming essential, and how it can transform your interactions with AI.

What is Prompt Engineering?

At its heart, prompt engineering is about precision in instruction. It’s the meticulous process of designing and refining the direct input – the “question” or “command” – given to an LLM to guide its output. Think of it as being a highly skilled interrogator, asking the exact right question to get the most accurate and relevant answer from a vast, intelligent database.

Richard Socher (Socher.org) founder of You.com, and former Chief Scientist at Salesforce (after his AI startup MetaMind was acquired), is widely credited as the inventor of Prompt Engineering. He famously said, “Prompt engineering is the new software engineering.” This highlights how crucial it is to craft effective prompts to get the best results from LLMs.

Prompt engineering involves understanding how LLMs interpret language, the nuances of their training data, and the specific capabilities of the model being used. It requires a blend of linguistic skill, domain knowledge, and an understanding of the model’s strengths and limitations.

The goal of prompt engineering is to minimize ambiguity and maximize the likelihood of the LLM generating a useful, coherent, and on-topic response. It involves techniques like:

• Using clear, concise language.
• Specifying the desired format (e.g., “list,” “summary,” “JSON”).
• Defining the persona of the model (e.g., “Act as a marketing expert”).
• Providing examples of desired output (few-shot prompting).

Many companies released detailed prompt engineering guides including OpenAI, Google, and Microsoft.

🎯 So What Is Context Engineering?

If prompt engineering is about asking the right question, context engineering is about building the right world for the LLM to exist in before it even considers the question. It’s the art and science of thoughtfully, precisely, and purposefully designing all the inputs that fill an LLM’s context window.

This goes far beyond just the prompt itself and encompasses a holistic approach to feeding information to the model. It includes:

• What data goes in (and what stays out): Carefully curating the relevant information, documents, and knowledge base that the LLM needs to draw upon. This involves filtering out noise and ensuring the data is accurate and up-to-date.
• How it’s formatted, ordered, and framed: Presenting the data in a structured, digestible manner. This could mean using markdown, specific delimiters, or a logical flow that helps the model understand relationships between pieces of information. The order in which information is presented can also significantly impact the model’s focus.
• What metadata, memory, constraints, and scaffolds guide the model: Providing additional layers of guidance.
  • Metadata: Information about the data (e.g., creation date, source, author) that helps the model understand its provenance and relevance.
  • Memory: Giving the model access to past interactions or ongoing conversational threads to maintain continuity and build upon previous knowledge.
  • Constraints: Setting boundaries or rules for the model’s output (e.g., “keep it under 200 words,” “do not mention competitor names”).
  • Scaffolds: Pre-defining the structure of the desired output, guiding the model to fill in specific sections (e.g., “Introduction, Body, Conclusion”).
• How the prompt is constructed in relation to the context: The prompt is still crucial, but now it acts as the final directive within a carefully constructed informational environment. It leverages the context to ask a question that the model is now uniquely equipped to answer with depth and nuance.

Here’s a simple way to understand the fundamental difference: If the prompt is the question, then the context is the world the model sees before answering.

🧠 Real-World Example: SEO A/B Test with LLMs

Let’s make this concrete with a scenario many businesses face: interpreting A/B test results. Imagine you run SEO for a high-growth B2B company. You’re testing two different landing page templates — Template A and Template B — to see which drives better engagement and conversion. You want an LLM to help interpret the results and recommend a path forward.

⚙️ Prompt Engineering Only

In a prompt-only approach, you might simply write:

“Interpret this A/B test and tell me which template is better.”

And perhaps the model, lacking deeper understanding of your business goals or the experiment’s nuances, responds with something like:

“Template A has a higher time-on-page, so it might be better. Check for statistical significance.”

Is this helpful? A bit. Is it actionable? Not really. It’s a generic observation that doesn’t account for your specific business context.

🧠 Now Enter Context Engineering

Now, let’s consider how context engineering transforms this interaction. You build a system that feeds the LLM not just a simple prompt, but a rich, structured context that sets the stage for a truly insightful analysis:

• Experiment Setup:
  • Hypothesis: Template A improves scroll depth and mobile engagement.
  • Key metrics: Bounce rate, time-on-page, form submissions.
  • Audience: Mostly organic, 70% mobile traffic.
• Side-by-side results:
  • A clean, tabular summary of Template A vs. Template B, including raw numbers and percentages.
  • P-values for key metrics, indicating statistical significance.
  • Annotations (e.g., “Template B got a traffic spike due to a recent blog mention, which might skew bounce rate”).
• Business Objectives:
  • Priority: Improve mobile user experience (UX) without hurting lead quality.
  • Constraint: Keep page speed under 2 seconds for optimal performance.
• Output scaffold:
  • “Insight Summary → Recommendation → Risks” (guiding the model to structure its response in a directly actionable format).

With this meticulously crafted context, the same LLM now delivers a dramatically different and far more valuable response:

• A nuanced comparison of metrics, taking into account the traffic spike for Template B.
• A recommendation rooted in your specific business goals (mobile UX, lead quality).
• A critical note that Template A performs better on desktop, but Template B wins on mobile, directly addressing your audience context.
• A proactive suggestion to A/B test a hybrid version, combining the strengths of both templates, which is a highly actionable next step.

Same model. Same core prompt. Massively different, and infinitely more valuable, outcome.

Example of Context Engineering in action.

For this prompt:

Let's get top 10 pages by source as well for all the top source/medium combinations from google analytics for the last 30 days. Give me a simple table comparing their performance across the important metrics like bounce rate, engagement, pages per session etc

The context given includes: A detailed instruction set on the role & purpose of this agent, context on how to pick and interpret the data from google analytics, and a structured output format.