In the early days of GenAI adoption, most of the effort went into building prompts, chaining tools, and selecting the right model. But as LLMs enter the enterprise mainstream, the biggest performance gains are coming from somewhere else: context engineering.
If prompt engineering is about how you ask, then context engineering is about what the model knows when you ask it.
And in many cases, context matters more.
What Is Context Engineering?
At its core, context engineering is the practice of deliberately shaping the information that large language models (LLMs) use to make decisions.
Every LLM request has a context window: a limited amount of information the model sees at inference time. This includes the current promept, system instructions, tool definitions, user preferences, and any retrieved knowledge from previous conversations or documents.
Context engineering is the art and science of curating that window. It’s about deciding what information goes in, how it’s formatted, and how it adapts over time based on user behavior, role, and task. It bridges the gap between general-purpose models and specific, contextualized enterprise use cases.
Without context engineering, even the best models will hallucinate, produce irrelevant outputs, or miss the mark entirely. With it, LLMs can feel far more intelligent, personalized, and reliable.
Why It Matters
Most enterprises aren’t struggling because they picked the wrong model. They’re struggling because their model doesn’t have the right context.
Common issues like hallucinations, irrelevant responses, or inconsistent behavior often stem from:
Missing task instructions (the model doesn’t know what it’s trying to do)
Lack of memory (the model forgets past conversations or decisions)
Insufficient knowledge (the model doesn’t know about your systems or business)
Poor tool definitions (the model doesn’t know how or when to use the tools it’s given)
Context engineering addresses all of these.
In other words, it’s not just a performance layer, it’s a trust layer that determines whether your AI feels like a prototype or a real assistant.
Why You Shouldn’t Roll Your Own
It might be tempting to build a custom context solution yourself. But the reality is that most hand-rolled solutions break down at scale:
They’re brittle and hard to maintain
They struggle to support multiple agents, tools, and personas
They often lack a governance model for what goes in or out of the context window
This is where Model Context Protocol (MCP) comes in.
MCP provides a structured, reusable, and observable approach to context engineering across your entire agent stack.
It standardizes how context is:
Defined (e.g. task instructions, user profile, tools, memory)
Assembled dynamically at runtime
Governed and validated
Updated over time
With MCP, you don’t need to reinvent context logic for each new use case. You define a pattern once and scale it with confidence.
Best Practices for Enterprise Context Engineering
Here’s what good context engineering looks like at the enterprise level:
Define roles and tasks: Context should adapt based on the persona and task (e.g. a financial analyst asking about Q2 trends).
Keep system instructions clean: Long, unfocused system prompts are hard to debug and instructions get lost in the context. Use short, layered instructions.
Version your tools and templates: Treat context like software. Use version control and structured testing.
Modularize your inputs: Use MCP or a similar structure to keep prompts, tools, memory, and retrieval decoupled.
Establish governance: Decide who can define or update context components — and how changes are reviewed.
Use telemetry: Track token usage, tool calls, and user satisfaction to tune your context dynamically.
Final Thoughts
Context engineering isn’t hype. It’s the natural evolution of building with LLMs.
As AI becomes embedded in real workflows, the difference between a helpful assistant and a frustrating one comes down to whether it understands the who, what, and why of each task.
You won’t get that from a bigger model. You’ll get it from better context.
And if you want to do that reliably, at scale, and with traceability, you need to engineer it.
At Fuse, we believe a great data strategy only matters if it leads to action.
