Context Engineering Will Eat Prompt Engineering for Lunch

Once upon a time — about, oh, 10 months ago — prompt engineering was the hottest thing in AI. Were you even trying if you weren’t reverse-engineering Shakespearean haikus into your prompts to trick a chatbot into helping you get a great response?

But now, there’s a new sheriff in town: context engineering. Unlike some of its predecessors, it’s not just a fad. It’s a structural shift in how we work with LLMs, and if your organization has file data (spoiler: it does), this shift matters more than you might think.

Let’s break it down.

Prompt Engineering: The Party Trick Era

In the early LLM days, we were all wizards casting spells. “Please write this email like Steve Jobs with a touch of Obi-Wan.”

Prompt engineering was a mix of charm, luck, and Stack Overflow.

But as the models got smarter, those clever tricks started losing their magic. Eventually, they just shrugged and got it wrong anyway. What they needed was relevant context.

Context Engineering Enters the Chat

Think of prompt engineering as giving instructions to a genius with amnesia. Context engineering, by contrast, is like setting that genius up with a whiteboard full of notes, diagrams, recordings, and examples before asking your question.

Andrej Karpathy said: “The LLM is like the CPU, and its context window is like RAM, representing a working memory for the model. Context enters an LLM in several ways, including prompts, user instructions, retrieval, e.g., documents, and tool calls, e.g. APIs. Just like RAM, the LLM context window has limited communication bandwidth to handle these various context sources. And just like an operating system curates what fits into a CPU’s RAM, we can think about context engineering as packaging and managing the context needed for an LLM to perform a task.”

And this is where file data comes in.

You know what’s rich in unstructured, high-value context? Your organization’s file data. You’ve got Word docs, CAD files, PDFs, spreadsheets, PowerPoints… and probably that one .txt from 2017 named finalfinalv3-revD.txt.

Why Context Engineering is Better for Enterprise AI

Here’s why this change is a tectonic shift for businesses:

• LLMs crave domain-specific knowledge
• Context beats cleverness
• Reliability matters

Nasuni: Your Context Delivery Engine

The Nasuni File Data Platform is an intelligent, cloud-native unstructured data management system that lets you unlock your file data at scale. All your unstructured data in one place – no more silos, no more redundant files. Think of it as the backstage crew making sure your AI actors always have the right script in hand.

Key advantages include:

• A unified global file system – one source of truth.
• Edge caching and performance – always feed AI the latest and freshest data.
• Built-in data security – your flow of data for AI must be resilient.
• Integration-ready – use the AI tools and services of your choice.

Your AI Doesn’t Need Better Prompts, It Needs Better Access

If you want LLMs to help your teams work smarter, you must start providing them the right context. And that means unlocking your file data, securely, and intelligently.

That’s why organizations are going with Nasuni. It’s the bridge between your AI ambitions and the ocean of unstructured knowledge your company is already sitting on.

For more insight into context engineering and why it is more important than prompt engineering, listen to the excellent The AI Daily Brief Podcast. Their June 25^th episode on the topic is helpful and informative.

Prompt engineering walked so context engineering could fly. And Nasuni? Well, it’s the runway.

Learn more about how Nasuni enables the delivery of your unstructured data to power your business like never before here. Or you can simply reach out. I’d love to chat.