There’s a pattern that shows up in almost every small business that’s started using Claude. A handful of people are getting remarkable results, producing better drafts faster, summarising complex documents in minutes, generating ideas that actually go somewhere. The rest of the team tries the same tool and gets output that’s generic, slightly off, or just not quite useful enough to bother with.
The difference usually isn’t the tool. It isn’t even the prompt.
It’s context.
What does context actually mean in AI?
Context, in the AI sense, is the background information you give a tool before asking it to do something. It’s the difference between walking into a meeting with someone who knows your business, your client, your objectives, and your constraints, and walking in with someone who’s just arrived and knows nothing.
If you ask Claude to “write a proposal for a new client” with nothing else to go on, it will produce something. It will be competent, reasonably structured, and completely generic. It won’t reflect your business’s approach, your client’s specific situation, the tone you prefer, or the outcome you’re actually trying to achieve. It’s the equivalent of asking a capable new team member to handle a piece of client work on their first day with no briefing whatsoever.
Now imagine giving that same person a proper briefing. Here’s the client. Here’s what they do. Here’s what they told us they need. Here’s how we usually approach this kind of work. Here’s what we want them to feel when they’ve read it. The output from that conversation looks entirely different, and so does the output from Claude when you give it the same level of grounding.
Why isn’t prompting alone enough?
Most of the early conversation about AI skills focused on prompting, the specific wording you use when you ask a tool to do something. There are guides, courses, and no shortage of social media posts dedicated to the idea that the right prompt unlocks dramatically better results.
Prompting does matter. But it’s one layer of a more complex picture, and treating it as the whole answer is where a lot of small business teams get stuck.
A well-written prompt with no context behind it is still asking Claude to work from a standing start. It might be a politely worded, well-structured request, but if Claude doesn’t know who you are, what you’re trying to achieve, who your audience is, or what constraints you’re working within, the output will reflect that gap.
Context is the layer that sits beneath prompting. It’s what makes prompting work properly, and it’s one of the reasons Claude in particular is worth learning well. Claude is unusually responsive to detailed context, the more grounding you give it, the more the quality of its output improves.
What does high-context AI use look like in practice?
Here’s a concrete example. A marketing lead at a small accountancy practice is asked to draft a thought leadership article for one of the partners. She asks Claude to help.
Low context:
“Write a thought leadership article about AI in financial services.”
Claude produces a generic, reasonably competent piece. It covers some of the obvious ground. It doesn’t sound like the partner. It doesn’t reflect the firm’s positioning. It would need such significant reworking that it would have been faster to write it from scratch.
High context:
“I’m writing a thought leadership article for one of the partners at our accountancy practice. His perspective is that AI is being adopted too fast in the sector without proper governance. The audience is finance directors at small and mid-sized UK businesses. The tone should be authoritative but not alarmist, he wants to challenge the rush to adopt without dismissing the technology. The article needs to be around 800 words and will appear on our website.”
Same tool. Significantly different output. The high-context version produces something that’s much closer to usable, something that reflects the partner’s actual point of view, speaks to the right audience, and requires editing rather than rewriting.
Where does context come from?
This is the practical question, and it’s worth spending time on.
Context comes from three main sources. The first is your understanding of the task: what you’re trying to produce, who it’s for, what it needs to do, and what constraints it’s working within. The second is your knowledge of the subject matter: what background information is relevant, what the key points are, what’s already been said or decided. The third is your understanding of your own business: voice, values, positioning, conventions, and history.
None of this is complicated. It’s essentially the information you’d put in a briefing document for a new team member. The shift that good AI training creates is the habit of thinking explicitly about that information before you start, rather than diving straight in and hoping Claude figures it out.
How do you build context into your team’s AI workflow?
Once you understand that context is the key variable, a lot of practical decisions become clearer.
It’s worth developing shared context documents for the things your team does regularly. A briefing template for client communications. A style guide that captures your business’s tone and conventions. A background document that explains who you are, what you do, and how you approach your work. These don’t have to be complex, a single page is often enough. But having them means anyone on the team can quickly give Claude the right foundation before they start work.
It also means thinking about context at a workflow level, not just a task level. If Claude is being used to support a recurring process, a weekly report, a client update, a monthly newsletter, it’s worth building the relevant context into the workflow itself, so it doesn’t have to be reconstructed from scratch each time. This is exactly what a Claude Project is for: a workspace that holds your context once and reuses it every time.
The takeaway
The teams that get the most from Claude aren’t necessarily the ones who’ve spent the most time learning about it. They’re the ones who’ve got better at explaining what they need, and that’s a skill every small business owner already has the raw material for.
If your team is finding that Claude produces generic, slightly-off output that needs more work than it saves, context is almost certainly where the gap is. That’s not a problem with the tool, and it’s not a problem with the people using it. It’s a gap in the shared understanding of what makes Claude work well, and it’s closable.
Building that habit, consistently, across a team, is the difference between Claude as a frustrating novelty and Claude as something that genuinely changes how people work. If you’re not sure where your team currently stands, a short conversation about where the gaps are is usually the most useful first step.
Context is one piece of the picture. The other is making sure your team has a shared framework for using AI safely and consistently — something most organisations haven't yet put in place. Your team is already using AI explores why that matters.