Getting Better Results from AI

If you’ve used Claude or ChatGPT and found the results mediocre, you’re not alone. The gap between underwhelming and genuinely useful output usually comes down to how you write your requests—not the tool itself.

You don’t need to learn “prompt engineering” as a discipline. A handful of practical habits will get you most of the way there.

Be specific about what you want

Vague requests produce vague results. This is the single biggest lever.

Weak: “Write a product description for this jacket.”

Stronger: “Write a 75-word product description for this jacket. The audience is outdoor enthusiasts who prioritize function over fashion. Focus on the waterproofing and packability. Avoid lifestyle language like ‘adventure-ready.’ End with a specific use case.”

The second version tells the AI what to write, how long to make it, who it’s for, what to emphasize, what to avoid, and how to end. That’s not a trick—it’s just being clear about what you need.

Give it context about your brand and customers

AI doesn’t know anything about your business unless you tell it. Include relevant context at the start of any conversation where brand consistency matters:

• What your brand voice is like (and what it’s not)
• Who your customer is
• What channel this is for
• Any specific terminology to use or avoid

You can create a short “brand context” block that you paste at the start of sessions:

We’re a mid-market outdoor apparel brand. Our voice is direct, practical, and confident—never flowery or aspirational. Our customer is 35-55, buys on function, and doesn’t respond to “adventure” or lifestyle messaging. We avoid superlatives. We prefer specifics: “fits in a jacket pocket” over “ultra-packable.”

The Document Brand Voice skill can generate this block from your existing content, which you can then reuse across sessions.

Show it an example of what “good” looks like

Examples are more effective than descriptions. If you have existing content you’re happy with, include it:

“Here’s a product description we like: [paste example]. Write three more descriptions in the same style for these products: [paste specs].”

The AI calibrates to your example rather than its defaults. This works especially well when your brand has a distinctive voice that’s hard to describe but easy to recognize.

Treat it as a conversation, not a one-shot query

Most people write one request, get a result, and either use it or don’t. That’s not how it works best.

Treat it like a back-and-forth:

1. Ask for a first draft
2. Tell it what to keep, what to change, and what’s missing
3. Ask for a revised version
4. Repeat as needed

“Good start—make it shorter, cut the second sentence, and lead with the price point instead of the product name.”

You don’t need to rewrite the entire prompt each time. Incremental refinement is faster.

Tell it what format you want the output in

If you need specific output for downstream use, specify the format:

• “Return the results as a markdown table with columns for Product Name, Meta Title, and Meta Description.”
• “Give me the output as a numbered list.”
• “Format this as a JSON object with keys: title, description, tags.”

Consistent output format makes it easy to paste results directly into a spreadsheet or system without reformatting.

Common mistakes ecommerce teams make

Asking it to make things “better” without defining better. Better for who? In what way? Specify what you mean.

Not providing the source data. If you want a product description, paste the spec sheet or product attributes. Don’t make it invent details.

Accepting the first output. The first draft is a starting point. One round of feedback usually produces significantly better results.

Using it for tasks outside its strengths. Asking AI to calculate accurate margin percentages or tell you what your competitors are charging right now will lead to disappointment. Stick to language tasks.

Treating every conversation as independent. Within a session, the AI remembers the conversation. Use that—give context once at the start, then build on it through the session rather than repeating yourself in every message.

A practical workflow

For most ecommerce content tasks, this structure works well:

1. Set context: Brand voice, audience, channel (one paragraph)
2. Provide the source material: Product spec, data, or brief
3. Make a specific request: Include format, length, and any constraints
4. Review and refine: Give targeted feedback, ask for a revision
5. Check before using: Verify any specific claims, especially specs and features

Once you’re comfortable with this approach, AI skills make it even easier—they handle the prompting structure for you. Read Installing and Using AI Skills to see how.