Context Engineering over Prompting?
Have you asked AI a question and you get the most generic answer? This is the way to fix it!
Welcome back to Tech Break by Friday. I promise to make it up to you for missing a week on uploading an episode. Today’s topic is a really exciting one for me. We talk about Context Engineering, and it changes the way we talk and think about AI. This is why some AI tools can provide the answer you need based on your situation, acting as a personal guide. Well, some other tools make you throw away your laptop, and you seem to get nowhere.
Have you ever asked ChatGPT or another AI tool to help with something specific to your business, only to get a response that's technically correct but completely useless? Like asking for help with your marketing strategy and getting generic advice that could apply to literally any business on the planet? That's not a model problem - that's a context problem. And today, we're going to solve it.
Here's a wild stat for you: According to industry leaders like Andrej Karpathy, who co-founded OpenAI, most AI failures aren't because the AI isn't smart enough. The issue is that the AI doesn't have the correct information at the right time.
Think about it like this: imagine hiring the world's most talented personal assistant, but every morning they walk into work with complete amnesia. I don’t remember yesterday, no understanding of your business, no access to your files or contacts. How helpful would they actually be?
That's how we've been using AI. And Context Engineering is how we fix it.
What Is Context Engineering?
So what exactly is Context Engineering? Let me give you the simple version first, then we'll get into why it's revolutionary.
Context Engineering involves designing and building dynamic systems that facilitate prompt engineering for LLMs because it focuses on providing all the necessary context for the task to be plausibly solvable by the LLM.
But here's where it gets interesting: this isn't just about writing better prompts. Most people think AI improvement is about finding the magic words to type into ChatGPT. Context Engineering is completely different. It's about building systems entirely.
As the experts at LangChain put it, Context Engineering is a system. Complex AI agents obtain context from multiple sources, including the application developer, the user, previous interactions, tool calls, and other external data. This requires sophisticated system architecture, not just clever writing. I’ll be sure to
Let me paint you a picture. Traditional prompt engineering is like trying to explain your entire business to someone in a single text message. Context Engineering is like giving that person access to your company database, your style guide, your customer history, and your business goals: then having them pull exactly what they need for each specific task.
Here's what makes this revolutionary: Context Engineering treats context as everything the model sees before it generates a response. This includes system instructions, user prompts, conversation history, long-term memory from previous interactions, retrieved information from databases, available tools the AI can use, and even the structured format for outputs.
As Andrej Karpathy, co-founder of OpenAI, perfectly summarized: Context engineering is the delicate art and science of filling the context window with just the correct information for the next step. Unlike static prompts, Context Engineering creates dynamic systems that provide the correct information and tools, in the right format, at the right time.
The key insight is that most agent failures are not model failures; they are context failures. When AI systems have failures, an encompassing, well-structured context, they can accomplish tasks that would be impossible with even the most carefully crafted prompt alone.
The Difference with Prompt Engineering
Here's the moment this clicked for me. I was talking to a business owner who was frustrated with AI tools. She said, "I asked it to write an email to our biggest client about a delay in their project. The AI wrote something that was grammatically perfect but sounded like it came from a robot who had never worked in our industry."
That's the difference between prompt engineering and context engineering. Prompt engineering would focus on writing a better request. Context engineering would give the AI access to:
Previous emails with that specific client
The company's communication style guide
Details about the specific project and the delay
The relationship history and context
Similar situations and how they were handled before
The result? An email from someone familiar with the business, the client, and the situation.
Real-World Examples
Let me give you three examples that show just how powerful this can be:
Example 1: Customer Support
Instead of a chatbot that gives generic responses, Context Engineering creates systems that instantly pull up:
The customer's account history
Their previous interactions with support
Current account status and any ongoing issues
Relevant help documentation for their specific situation
Even the customer's preferred communication style based on past interactions
Example 2: Content Creation
Rather than asking AI to "write a social media post about our product," Context Engineering provides:
Your brand voice guidelines and examples
Recent post performance data
Current trending topics in your industry
Your audience demographics and preferences
Your content calendar and upcoming campaigns
Example 3: Business Strategy
Instead of generic business advice, Context Engineering gives AI access to:
Your actual financial data and trends
Competitor analysis and market positioning
Your team's capabilities and resources
Previous strategic decisions and their outcomes
Current market conditions and opportunities
Why This Matters Right Now
Here's what's fascinating - and this comes straight from Tobi Lütke, the CEO of Shopify, who really sparked this conversation on social media in June 2025. He said that Context Engineering "describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM."
This was immediately endorsed by Andrej Karpathy, co-founder of OpenAI, who called it 'the delicate art and science of filling the context window with just the right information for the next step'.
And here's why this matters right now: As AI researcher Philipp Schmid points out, 'Most agent failures are not model failures anymore, they are context failures'. Companies mastering Context Engineering are achieving 3x faster task completion and significantly better results.
The Technical Reality
Now, I don't want to get too technical here, but understanding the basics will help you see why this is so important.
AI models like GPT-4 or Claude are prediction machines. They look at the text you give them and predict what should come next based on patterns they learned during training. But here's the key - they're stateless. That means they don't remember anything between conversations, and they only know what's in their immediate "context window”,think of it like their short-term memory.
So when you ask an AI to help with your business, it's not pulling from some magical database of business knowledge. It's working purely with:
What was learned during training (which might be outdated)
What you've told them in your current conversation
Context Engineering changes this by systematically providing the AI with current, relevant, verified information for each specific task. Instead of the AI guessing or making things up, it's working with your actual data.
The Business Impact
Let's talk about what this means for your business. Companies that implement Context Engineering are seeing:
Drastically improved accuracy in AI-generated content and recommendations.
Reduced time spent fact-checking and correcting AI outputs
More consistent brand voice across all AI-assisted communications
Better strategic insights because AI is working with real data, not assumptions
Increased trust in AI tools because outputs are reliable and relevant
But here's the kicker: this isn't just about making existing AI tools better. Context Engineering enables entirely new types of AI applications that can actually take meaningful action in your business.
Action Steps for You
So what can you do right now? Here are three concrete steps:
Step 1: Audit Your Current AI Usage
Look at the AI tools you're currently using. Ask yourself: What information does this AI need to be truly helpful to my specific situation? What's missing?
Step 2: Start Building Context Libraries
Begin collecting and organizing the information that would make AI more useful for your business:
Brand voice examples and guidelines
Customer personas and research data
Previous successful projects and outcomes
Industry-specific terminology and standards
Step 3: Think Systems, Not Prompts
Instead of focusing on crafting the perfect prompt, consider how you can systematically provide AI with the right context. This might mean integrating with your existing business tools and databases.
This is not sponsored, but you can use Claude Projects or Perplexity Spaces to add files and information you need for context. Test how the answer might differ when you use more context. Think of systems and track your prompts, basically, what works and what doesn’t work. See what improvements you can make.
Future Implications
Looking ahead, Context Engineering will be the foundation for AI agents that can actually understand your business and take meaningful action. We're transitioning from AI that provides generic advice to AI that can make informed decisions tailored to your specific situation, goals, and constraints.
The businesses that master this now will have a massive advantage as AI capabilities continue to expand. They'll have AI tools that truly understand their context, their customers, and their goals.
Context Engineering represents a fundamental shift in how we think about AI - from trying to find magic prompts to building intelligent information systems. It's the difference between having an AI assistant who's constantly confused about what you need and one who truly understands your business.
If you take away one thing from today's episode, let it be this: The future of AI isn't about more innovative models - it's about better context. The companies and individuals who understand this will be the ones who actually get value from AI, while everyone else is still struggling with generic, unhelpful responses.
That's all for today's episode of Tech Break by Friday. If this resonated with you, I'd love to hear about your experiences with AI context in your business. Reach out on Instagram or email us at techbreakbyfriday@gmail.com.
Until next time, keep questioning, keep learning, and remember - the most powerful AI isn't the smartest one, it's the one that knows exactly what you need.
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Sources:
https://arxiv.org/abs/2507.13334
.https://www.philschmid.de/context-engineering