Written By Naren Kumar
Large language models like ChatGPT and Claude are typically associated with quick content generation. While being able to create content quickly is valuable, depending completely on LLMs for this can result in the production of content that lacks depth and emotional connection. This doesn’t mean that brands need to stop using these models for content altogether. They can still use them for research. LLMs can help support research, find detailed insights, and make overall work easier. Let’s understand how brands can use LLMs to scale research.
Understanding Customer Feedback
Customer feedback is valuable. However, when there are a lot of reviews, surveys, support tickets, and open text responses to read, it becomes a big challenge to go through them all. It can be time-consuming and often unrealistic to go through so much feedback. LLMs can help process customer feedback quickly and identify the commonly mentioned themes and messages in it. With this data, brands can refine their content and align it more according to customers’ preferences. However, brands shouldn’t trust AI blindly; they should create a structured format for placing all the feedback and let LLMs run through the data. In this way, they can double-check to see if LLMs are processing and interpreting data correctly.
Capturing Expert Insights
Subject matter experts help create accurate and trustworthy content, but they are often short on time and are unable to participate in long interviews. LLMs can act as structured interviewers, making the process more organized. An AI-driven interview process can allow experts to answer focused questions according to their pace. This way, they can share valuable insights in short sessions instead of long interview calls. Once responses are collected, LLMs can summarize the main points and insights, transforming them into usable content outlines.
Learning from Competitor Analysis
LLMs can also help with competitor research. By understanding how competitors are positioning themselves, how they are responding to customers, and what strategies they are using, brands can improve their own content strategy. LLMs can help uncover gaps in the market, allow brands to understand what their competitors are focusing on, the audiences they are targeting, and the mistakes they are making. By learning their strategies and mistakes, brands can avoid making the same errors and position themselves better in the market.
Scaling Research, Staying Human
Depending only on numbers often doesn’t tell the full story. A major risk of using AI in research is losing the emotional context behind the data. To make content more human, brands should focus on qualitative and customer-led data sources like support interactions, on-site search behavior, and direct feedback. LLMs can help analyze these sources by capturing the intent and sentiment of the data. When teams review this data themselves, along with the insights brought by LLMs, they can move faster without disconnecting from the real customer experiences. This way, AI can enable teams to scale research while keeping content human. However, it’s important to strike a balance and always involve human review and understanding in research.
At TechnoRadiant, we believe the future of content lies in combining human creativity with AI-powered research. By following a balanced approach, brands can scale their content efforts without losing trust, clarity, or meaningful connection with their audience.
