Context Engineering: The Foundation of Effective LLM Interactions

Why Context Matters More Than You Think

In the rapidly evolving world of large language models (LLMs), we often focus on prompt engineering—crafting the perfect question or instruction. However, there's a more fundamental skill that underpins every successful interaction with AI: context engineering.

Context engineering is the art and science of providing LLMs with the right information, at the right time, in the right format. It's the difference between receiving a generic response and getting genuinely useful, tailored output that meets your specific needs.

What Is Context Engineering?

Context engineering involves deliberately structuring and organising the information you provide to an LLM before asking it to perform a task. This includes:

• Relevant background information about your project, domain, or situation
• Examples and samples that demonstrate the desired output format or style
• Constraints and requirements that guide the model's behaviour
• Domain-specific knowledge that might not be in the model's training data
• Previous conversation history that provides continuity

The Context Window: Your Workspace

Modern LLMs have what's called a "context window"—the amount of information they can process in a single interaction. Think of it as working memory. Recent models boast impressive context windows of 200,000 tokens or more, which is roughly equivalent to a 500-page novel.

However, having a large context window doesn't mean you should fill it indiscriminately. Quality trumps quantity. A well-curated 2,000-token context often outperforms a rambling 20,000-token dump of loosely related information.

Why Context Engineering Is Critical

1. Precision and Relevance

Without proper context, LLMs fall back on general knowledge and common patterns. This might work for generic queries, but it fails spectacularly for specialised tasks. Imagine asking an LLM to refactor code without showing it the existing codebase—you'd get textbook solutions that don't fit your architecture, naming conventions, or design patterns.

2. Consistency Across Interactions

Context provides continuity. When working on a multi-step project, maintaining relevant context ensures the LLM doesn't contradict itself or forget important decisions made earlier. This is particularly crucial in software development, where architectural choices cascade through the entire codebase.

3. Reducing Hallucinations

LLMs sometimes generate plausible-sounding but incorrect information—a phenomenon called "hallucination." Providing concrete, factual context grounds the model's responses in reality, significantly reducing the likelihood of fabricated information.

4. Domain Adaptation

Your project, company, or field likely has specific terminology, conventions, and requirements. Context engineering allows you to effectively "tune" a general-purpose LLM to your domain without requiring model fine-tuning or retraining.

Best Practices for Context Engineering

Be Specific and Structured

Don't just dump information. Organise it logically. Use headings, bullet points, and clear labels. If you're providing code, include file names and relevant line numbers. If you're sharing documentation, maintain its original structure.

Prioritise Relevance

Include what's directly relevant to the task at hand. If you're debugging a specific function, the LLM doesn't need to see your entire application—just the function, its dependencies, and perhaps related test cases.

Provide Examples

One good example is worth a thousand words of explanation. If you want the LLM to follow a particular coding style, show it examples of that style. If you want a specific output format, provide a sample.

Update Context as You Go

Context isn't static. As your conversation evolves, refresh the context with new information, updated requirements, or recent developments. Remove outdated context that might confuse the model.

Use Metadata

When providing files or code snippets, include metadata: file paths, timestamps, version numbers, or dependency information. This helps the LLM understand relationships and make informed decisions.

Context Engineering in Practice

Consider two scenarios for adding a feature to a web application:

Poor Context:
"Add a login button to my app."

Rich Context:
"I'm working on a React application using TypeScript and Material-UI. I need to add a login button to the navigation bar component (src/components/Navbar.tsx). The button should match our existing design system—specifically, it should use the primary colour variant with medium size, consistent with our other action buttons in the UserProfile component. When clicked, it should navigate to '/auth/login' using our existing React Router setup."

The second request provides the LLM with framework information, file locations, design requirements, and integration details. The result will be far more useful and require minimal adjustment.

The Future Is Context-Aware

As LLMs become more sophisticated, they're increasingly designed to work with rich context. Tools like Claude Code can maintain awareness of entire codebases, previous interactions, and project-specific conventions. The developers who master context engineering will be the ones who extract maximum value from these tools.

Conclusion

Context engineering isn't just a nice-to-have skill—it's fundamental to effective LLM usage. By thoughtfully curating the information you provide, you transform an LLM from a generic assistant into a knowledgeable collaborator who understands your specific needs, constraints, and goals.

The next time you interact with an LLM, don't just focus on crafting the perfect prompt. Ask yourself: "What context does the model need to truly understand what I'm asking for?" The answer to that question will dramatically improve the quality of your results.

After all, in the world of AI assistance, context isn't just king—it's the entire kingdom.