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How Do I Refine AI Results for More Accurate Interpretations?

Updated over a month ago

The AI features in BrandMentions, such as AI Analysis and the BrandMentions AI Assistant, can give you fast, powerful insights. To make those insights as accurate and relevant as possible, you need to guide the AI a little.

You do not have to be technical. You just need to:

  • Ask clear, specific questions

  • Add context

  • Use filters

  • Treat the AI as a conversation, not a one time query

This guide shows you practical techniques to refine AI results so you get better, more actionable interpretations.

Go to the AI Assistant

Key techniques to refine AI results in BrandMentions

1. Ask specific and well defined questions

This is the most important technique for getting accurate answers.

Instead of:

"What are people saying about our brand?"

Ask something like:

"What are the main complaints about our customer service on Twitter in the past month?"

Or:

"What are the top positive themes in mentions about our new product launch in the last 7 days?"

Why this helps

  • Specific questions reduce ambiguity.

  • The AI can focus on the right segment of your data.

  • You get targeted, useful insights instead of generic summaries.

Use clear elements in your questions, such as:

  • Topic: customer service, pricing, delivery, product X, campaign Y

  • Time frame: last week, last month, Q1, past 7 days

  • Channel or source: Twitter, Instagram, news sites, all sources

  • Region (if your setup supports it): the United States, Europe, a specific country

2. Use a conversational and iterative approach

Do not treat the AI Assistant as a one and done Q and A box. Treat it as a conversation.

For example:

  1. Start broad:

    "What are the main reasons for negative sentiment about our brand in the last 30 days?"
    ​

  2. Then refine:

    "Focus only on social media."
    "Now show me examples from the United States."
    "Can you group these reasons into 3 main categories?"
    ​

  3. Then go even deeper:

    "What actions would you recommend to reduce complaints about delivery?"

Why this helps

  • You shape the result step by step.

  • Each follow up gets you closer to exactly what you need.

  • You can correct or redirect the AI without starting from scratch.

Useful follow up prompts:

  • "Can you elaborate on that point?"

  • "Filter this to only include negative mentions."

  • "Show examples of mentions that support this insight."

  • "Explain this in simple language for a non technical stakeholder."

3. Provide clear context in your questions

When you ask about a specific event, campaign, or product, tell the AI.

Instead of:

"How did our new campaign perform?"

Ask:

"How did our 'Spring Sale 2025' campaign perform on social media between March 1 and March 31?"

Or:

"Summarize what people are saying about the launch of our mobile app, which started on 10 April."

Why this helps

  • Context tells the AI where to look in the data.

  • You avoid mixing conversations from different time frames or topics.

  • You get answers that match your internal naming and time periods.

Good context elements to mention:

  • Campaign or product name

  • Launch or campaign dates

  • Channels used

  • Any known incident or change you are investigating

From passive to active use of AI

By using these techniques you move from being a passive consumer of AI results to an active partner in the analysis process:

  • You tell the AI what matters.

  • You narrow the scope.

  • You test, refine, and verify the answers.

This leads to:

  • More accurate interpretations

  • More relevant insights for your team

  • Stronger, data driven decisions for your brand

By guiding the AI with clear questions, context, filters, and feedback, you get results that are not only smart, but also truly useful for your business.

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