“Help me with a presentation.” Five words, zero useful context. Claude will give you something back, but it’s essentially guessing your audience, your goals, and your format all at once. Compare that to a prompt that specifies ten slides, a quarterly sales audience, and three key topics. Same model, wildly different output.
We’ve been using Claude heavily across our own projects at Baur Software, for everything from drafting client deliverables to analyzing data to planning sprints. Along the way, we’ve learned that the quality of what you get back almost always comes down to the structure of what you put in. Every detail you leave out is a decision you’re delegating to the AI. Sometimes that’s fine. Often, it’s not.
Here’s the framework we’ve landed on.
The SCOPE Framework
We find it helpful to think about effective prompts across five dimensions. We call it SCOPE:
Specifics — What exactly do you need? Nail down the task, the deliverable, and the constraints.
Context — Who is this for? What’s the situation? What background does the model need?
Output format — How should the response be structured? A table? Bullet points? Two paragraphs? A slide outline?
Perspective — Should the model adopt a role or point of view? (“Act as a CFO reviewing this report.”)
Examples — Can you show what good looks like? Even one example dramatically improves output quality.
You don’t need all five every time. But when a prompt isn’t working, check which of these you’re missing — that’s usually where the problem is.
Before and After: The Difference in Practice
Let’s look at three real scenarios where applying SCOPE transforms the output.
1. Content Creation
Before:
Write something about cybersecurity.
This could produce anything from a children’s explanation to a PhD thesis. Claude has no way to calibrate.
After:
I need a blog post about cybersecurity best practices for small
business owners. The audience isn't very tech-savvy, so the content
should be easy to understand, practical with actionable tips, and
slightly humorous to keep their interest.
Provide an outline for a 1,000-word post covering the top 5
practices these business owners should adopt.
What changed: we added context (small business owners, not tech-savvy), specifics (top 5 practices, 1,000 words), and output format (an outline). The model now knows exactly what lane to stay in.
2. Data Analysis
Before:
Analyze our sales data.
After:
I've attached a spreadsheet called 'Sales Data 2023'. Analyze this
data and present findings in this format:
1. Executive Summary (2-3 sentences)
2. Key Metrics: total sales per quarter, top product category,
highest growth region
3. Trends: 3 notable trends with brief explanations
4. Recommendations: 3 data-driven suggestions with rationale
Then suggest three types of visualizations that would communicate
these findings effectively.
What changed: instead of an open-ended request, we defined a specific output structure with clear sections. We also referenced the document by name and asked for actionable recommendations — not just description.
3. Strategic Thinking
Before:
Help me prepare for a negotiation.
After:
You are a fabric supplier for my backpack manufacturing company.
I'm preparing for a negotiation to reduce prices by 10%. As the
supplier, provide:
1. Three potential objections to our price reduction request
2. For each objection, a counterargument from my perspective
3. Two alternative proposals you might offer instead of a
straight price cut
Then switch roles and advise me, as the buyer, on how to best
approach this negotiation.
What changed: perspective does the heavy lifting here. By asking the model to role-play as the other side first, you get objections and counterarguments that feel realistic rather than generic. The role-switch at the end gives you a practical game plan.
The Refinement Loop
Even great prompts sometimes need a second pass. The key is giving specific feedback rather than vague dissatisfaction.
Don’t say:
Make it better.
Say:
Good start. Please adjust:
1. Make the tone more casual and friendly
2. Add a specific customer example in the second section
3. Shorten the second paragraph — focus on benefits, not features
Think of it like giving feedback to a colleague. “This isn’t quite right” is unhelpful. “Move the conclusion up and cut the jargon” is actionable. The same principle applies here.
Quick-Reference: Task-Specific Tips
For summaries and document Q&A: Specify the focus (“summarize the AI trends section in 2 paragraphs”), ask for citations (“reference specific pages”), and name the document explicitly.
For brainstorming: Set a quantity target (“give me 10 ideas”), ask for categorization (“group by difficulty” or “indicate which are low-cost”), and request brief rationale for each suggestion.
For comparisons: Ask for table format with specific criteria. Instead of “compare these tools,” say “compare Asana, Trello, and Microsoft Project across pricing, scalability, ease of use, and best-fit team size.”
When Things Go Wrong
Three common failure modes and how to fix them:
The model is confident but wrong. Add this to your prompt: “If you’re unsure about any of this, say so rather than guessing.” Claude will flag uncertainty when given explicit permission to do so.
The response is too broad or shallow. Your prompt is probably too broad. Break it into steps. Instead of asking for an entire marketing strategy in one shot, work through market analysis, then audience definition, then channel selection — one at a time.
The model seems to forget context. Claude doesn’t carry memory between conversations. If you’re starting a new chat, re-include all relevant background. Don’t assume it “remembers” your previous session.
The Takeaway
Writing effective prompts isn’t about gaming the system or memorizing magic phrases. It’s about clear communication: telling the model what you need, who it’s for, and what the output should look like.
Run through SCOPE next time you’re drafting a prompt. If the result still isn’t right, refine with specific feedback. Two well-structured turns will almost always outperform ten vague ones.
The models are getting smarter every month. The bottleneck is increasingly on the input side — and that’s entirely within your control.
This post is part of an ongoing series where we share what we’ve learned building real projects with Claude at Baur Software. We’ve covered why your AI coding setup matters and how AWS Bedrock and Claude Code form the infrastructure layer underneath it all. Prompting well is the next piece of the puzzle. But knowing how to talk to the model only gets you so far. At some point the thing you’re building gets complex enough that good prompts aren’t a substitute for good architecture. When your vibe-coded prototype starts looking less like the balloon you imagined and more like a grenade with sawblades glued to it, that’s where we come in.