By Josh Tandy
A lot of attention is given to the importance of prompting large language models (LLMs) today. Say this, give exact instructions, use negative anchors, define measurable results, specify the format, and so on.
And while all of this is important to how your AI model or agent performs, there's something arguably more important that often gets overlooked.
Context.
To keep things simple, I'm going to use context to describe information an AI has available beyond your immediate request. That includes information retrieved through RAG (Retrieval-Augmented Generation), which is simply a way for an AI to search relevant documents or stored information and bring that material into its working context when needed.
It also includes memories of past projects, user preferences, writing styles, previous decisions, things you've approved or rejected, and information carried forward from earlier conversations.
Not every AI system supports persistent memory, and not all memory systems work the same way. But the underlying idea is straightforward.
Prompting helps you get repeatable results. Context helps you get results that are actually yours.
Let's look at an example.
You ask an AI to make a flyer for an event.
You give it the event name, date, location, and maybe a few instructions about what you want.
The model produces a flyer. It's probably decent. The information is there, the layout works, and it looks professional enough.
It also looks like approximately every other AI-generated flyer on the internet in 2026.
Now consider the same request with a model that has some history with you.
It knows you prefer certain layouts and colors. It knows you dislike excessive gradients, cluttered designs, and walls of text. It has examples of flyers you've approved and, perhaps more importantly, examples you've rejected.
You give it the exact same prompt.
The result can be dramatically different.
Not because you suddenly became an expert at writing prompts, but because the model has information about what you actually consider a good result.
And that distinction extends well beyond graphic design.
Imagine asking an AI to help you write an email. Without context, it might produce a perfectly polished message full of corporate language, generic introductions, and phrases no normal person would use in conversation.
With context, it can know that you prefer direct, conversational writing, dislike unnecessary formality, and would rather sound like yourself than a marketing department.
Same request. Different result.
That's the value of context.
So how do you actually use it?
There are several approaches, and you don't need to understand the underlying technology to benefit from them.
1. Build a relationship with your working environment
One of the simplest approaches is to use the same AI environment consistently, particularly one that supports persistent memory.
Over time, communicate your preferences, goals, and expectations.
When something is good, say so. When something is bad, explain why.
And be specific.
"This looks terrible" tells the model very little.
"I don't like the oversized heading, the unnecessary gradients, or the way everything is centered. I prefer a cleaner layout with stronger typography and more whitespace."
That's useful information.
The same applies to writing, programming, research, or practically anything else.
Don't just correct the immediate output. Explain the reasoning behind your correction.
If the system can retain that information and retrieve it later, you're gradually building a working environment that understands your preferences.
There's an important catch, though. Simply using the same model doesn't guarantee it remembers anything. Persistent memory depends on the application, its settings, and how information is stored and retrieved.
2. Bring your own context
This is one of the easiest and most reliable ways to improve results.
You don't have to depend on an AI remembering everything about you.
Create a simple .md or .txt file containing information relevant to what you're working on.
For example, if you're using AI to help write articles, that file might include your preferred tone, audience, examples of previous writing, things to avoid, and a few articles you're particularly happy with.
Attach that file when starting a new conversation.
Now the model has something concrete to work from.
The same approach works beautifully for software development.
You can provide an architecture document, project goals, design constraints, known problems, and previous engineering decisions.
Instead of spending half your conversation explaining what you've already built, you give the model the information it needs up front.
And here's something worth remembering.
A well-organized context document can be more useful than an elaborate prompt.
It doesn't need to be fifty pages long. In fact, I'd generally start small. A few pages of relevant, accurate information are often better than dumping an entire project history into the conversation.
3. Give examples, not just instructions
People often spend enormous amounts of time trying to describe exactly what they want.
Sometimes it's easier to show it.
If you're asking an AI to write something, provide examples of writing you like.
If you're asking for a design, provide examples of layouts you approve.
If you're building software, provide examples of the interface, behavior, or architecture you're trying to achieve.
And don't overlook negative examples.
Showing a model something you've rejected, along with an explanation of why, can be extremely useful.
The important part is explaining what the examples demonstrate. Otherwise, the model may imitate superficial details instead of understanding the underlying preference.
4. Keep context organized and relevant
More context isn't automatically better.
This is something that gets lost in discussions about models with enormous context windows.
Just because a model can accept hundreds of thousands of tokens doesn't mean you should fill every available token.
Irrelevant information can distract from the task. Outdated information can introduce mistakes. Contradictory instructions can make behavior inconsistent.
Think of context as a working reference library.
You want the model to have access to the right information at the right time, not necessarily every piece of information you've ever collected.
For larger projects, I prefer separating information by purpose.
A software project might have one document for architecture, another for design rules, and another for current development status.
A writing project might have a style guide, a collection of approved examples, and background research.
This also makes the information easier to maintain as things change.
5. Correct the context, not just the output
This is probably one of the most overlooked habits.
Suppose your AI keeps producing something you don't like.
You correct it. It produces something better.
Great.
But what happens next week when you start a new conversation?
If the system didn't retain the correction, you're right back where you started.
The better approach is to identify what information was missing or incorrect.
Was your style preference unclear? Was the project documentation outdated? Did the model retrieve an old design decision? Was an important constraint never recorded?
Fix that.
A corrected answer solves today's problem. Corrected context can prevent the same problem tomorrow.
Context isn't magic
There's one thing I want to be clear about.
Context doesn't guarantee accuracy.
A model can misunderstand your preferences, retrieve irrelevant information, rely on outdated memories, or confidently use information that was wrong to begin with.
And memories aren't necessarily a perfect record of past conversations.
This is particularly important when using AI for technical, financial, legal, or other consequential work.
Relevant context can make a model much more useful, but it doesn't eliminate the need to verify its work.
You also need to be thoughtful about what information you provide. Sensitive documents, customer records, private communications, and proprietary data deserve appropriate privacy controls.
The goal isn't to tell an AI everything about your life.
It's to give it the information necessary to do the job well.
The part people miss
I think we're spending too much time teaching people how to phrase individual requests and not enough time teaching them how to build useful working context.
Prompting matters. A poorly defined request can still produce a poor result, regardless of how much the model knows about you.
But there are limits to what you can accomplish by endlessly rewriting the same prompt.
At some point, the problem isn't how you're asking.
It's what the model doesn't know.
And that's where context becomes so valuable.
You don't need to become a prompt engineer. You don't need to memorize magic phrases or follow some ridiculous twenty-seven-step prompting framework.
Start with a clear request.
Give the model relevant information.
Show it examples.
Correct it when it's wrong.
And, when your tools support it, preserve what you've learned so you don't have to start over every time.
The difference between an AI that produces generic answers and one that becomes genuinely useful often isn't the model itself.
It's the quality of the information available to it.
Prompting is gold. Context is king.
And knowing how to use both is where things start getting interesting.