Context engineering is the skill of providing all the necessary context for large language models (LLMs) to perform specific tasks effectively. Unlike prompt engineering, which focuses on crafting the perfect single instruction, context engineering involves designing the entire information environment that an AI sees before generating a response.
The distinction matters because modern AI systems can now handle vast amounts of context – tens of thousands of words across dozens of pages. This capability has fundamentally changed how we should approach AI communication, shifting from clever one-line prompts to comprehensive context provision.
Understanding Prompt Engineering
Prompt engineering refers to the art of writing effective prompts for ChatGPT and other large language models. In the early days of AI chatbots, this typically meant crafting a single question or command to guide the model toward the desired output.
Prompt engineering works well for simple tasks, but it treats each interaction in isolation. Historically, it required repeating context every time because models had limited memory beyond their conversation window.
The approach focuses on finding the right wording within a single text string. For example, you might prompt: “Write a friendly follow-up email about our meeting last week, asking if the project proposal is approved.” The effectiveness relies heavily on exact phrasing and keywords.
However, prompt engineering has limitations. A single prompt can only contain so much information, and early language models had relatively small context windows of just a few thousand tokens.
@tjrobertson52 Context engineering is the AI skill that actually matters 🧠Better than prompt engineering? #ContextEngineering #AI #ChatGPT #Productivity #TechTips ♬ original sound – TJ Robertson
What is Context Engineering?
Context engineering is a broader, more comprehensive approach that emerged as LLMs became more powerful. Rather than focusing only on a single prompt, context engineering involves designing the entire information environment that the AI sees before producing an answer.
As Shopify’s CEO Tobi Lütke described it, context engineering is “the art of providing all the context for the task to be plausibly solvable by the LLM.” This means giving the model every bit of background knowledge, instruction, and example it might need to succeed.
The context for an LLM includes multiple components:
- Initial system instructions
- User prompts
- Conversation history
- Long-term background information
- Retrieved documents
- Available tools or functions
- Guidelines for output format
Context engineering orchestrates all these elements together so the AI has everything it needs to understand nuance, remember prior interactions, use relevant data, and perform complex tasks effectively.
Key Differences Between Prompt and Context Engineering
Scope of Input
Prompt engineering focuses on a single prompt string – a one-time instruction or question. Context engineering deals with the entire input context, which could include system instructions, conversation history, retrieved data, examples, and more. It’s a system-level approach rather than a single-query approach.
Information Provided
In prompt engineering, you try to cram all necessary details into one prompt. In context engineering, you feed the AI multiple layers of information systematically. The context engineer ensures the model isn’t missing crucial details, avoiding the “garbage in, garbage out” problem.
Static vs Dynamic
A prompt-engineered solution is often a static template used repeatedly. Context engineering is usually dynamic and adaptive – the system might retrieve fresh information or adjust the context for each new question.
Complexity and Tools
Prompt engineering is primarily about wording and formatting text. Context engineering is interdisciplinary – it involves aspects of information retrieval, memory management, and tool use in addition to phrasing.
As AI researcher Andrej Karpathy noted, in any “industrial-strength” LLM application, “filling the context window with just the right information” (such as task descriptions, explanations, few-shot examples, retrieved data, tools, state, history) is a delicate art and science.
Outcome Quality
Because context engineering gives the model richer background, it generally leads to more reliable and tailored outputs for complex tasks. Industry experts observe that context quality now determines performance outcomes more than model selection or prompt optimization for advanced applications.
Why Context Engineering is Emerging Now
Context engineering gained popularity in 2024-2025 due to several technological advances:
Larger Context Windows
Modern LLMs can handle vastly more text than earlier models. GPT-3 was limited to about 2,000 tokens, while GPT-4 can accept 8,000 or even 32,000 tokens, and newer models boast 100,000+ token contexts. This exponential increase means we can feed much more background information into the model.
AI Agents and Complexity
Today’s AI applications are more ambitious. We’re building agents that perform multi-step tasks like booking travel, writing code, or handling customer service. These agentic AIs need to consult various data sources, remember conversation history, and use tools. A simple prompt isn’t enough – the AI needs dynamic information retrieval, memory, and tool outputs as context.
Recognition of Prompt Limits
Through experience, developers realized that many AI failures came from inadequate context, not just poor wording. A cleverly worded prompt alone is often insufficient for complex tasks. The same prompt can yield great results or nonsense on different tries if the underlying context isn’t controlled.
As Armand Ruiz (VP of AI at IBM) remarked, “In the AI gold rush, most people focus on the LLMs. But in reality, context is the product.”
The Human Communication Connection
Context engineering isn’t entirely new – it’s essentially applying communication skills we already use with humans. When working with another person on a new task, you don’t just give a one-line instruction. You provide background, clarify constraints, show examples, and ensure they have the necessary resources.
The better you become at working with LLMs, the better you become at working with humans. There’s a natural tendency to be lazy with instructions, but whether you’re working with a human or large language model, it’s worth spending 5-15 minutes providing all the instruction and context needed for good results.
For tasks you’ll repeat multiple times, it might be worth spending 30 minutes, an hour, or even an entire day putting together specific instructions, context, and examples. This upfront investment pays dividends in consistent, high-quality outputs.
Best Practices for Context Engineering
Clarity and Precision
Be clear and unambiguous in your instructions. Define any terms that could be interpreted differently. Every sentence should have a purpose and clear meaning.
Structured Organization
Organize context logically using headings, bullet points, and sections. You might have separate areas for “Instructions/Rules,” “Background Information,” and “Examples.” Consistent formatting helps the model parse context correctly.
Relevant Detail Management
Know what information to include and exclude. Identify the relevant facts, constraints, or examples the AI needs without overloading it with irrelevant noise. Sometimes you’ll need to summarize or compress information due to token limits.
Few-Shot Examples
Provide a few examples of the task with input-output pairs. Examples should be representative and cover edge cases when possible. This is analogous to teaching by example – a common human teaching technique.
Iterative Testing
Treat prompts and context as living documents that can be tested and improved. Validate the AI’s output against your expectations. If results are wrong or off-target, analyze why and refine the context accordingly.
The Future of AI Communication
Context engineering represents the evolution from treating AI as a simple question-answering system to working with it as an intelligent collaborator. Instead of hoping the model figures things out from a short prompt, you proactively supply the right information in the right format at the right time.
This shift recognizes that providing comprehensive context is both possible with modern AI capabilities and necessary for reliable results. As AI systems continue advancing, those who master context engineering will unlock higher levels of performance, creating solutions that are far more accurate, coherent, and reliable.
The rise of context engineering also highlights a valuable insight: good AI communication mirrors good human communication. By learning to better instruct our machines, we’re reminded of how to better instruct each other – with clarity, patience, and complete context.
Ready to Improve Your AI Optimization?
Context engineering is just one aspect of how AI is transforming digital marketing. At TJ Digital, we help businesses stay ahead of these changes by optimizing for AI algorithms across platforms like Google’s AI search results, ChatGPT, and social media algorithms.Whether you need help with AI optimization, traditional SEO, or developing AI-powered content workflows, we’re here to help your business show up where your customers are searching. Contact us today to learn how we can improve your digital marketing results.