Building FeedSense.ai, a feedback management tool, has been an exciting journey. At its core, FeedSense.ai uses AI to transform raw user feedback into actionable tasks, making it easier for businesses to improve their websites and apps. In this blog, I want to share how I’m leveraging Llama 2 AI to power the feedback analysis process.
The Problem with Traditional Feedback Management
Most businesses struggle to make sense of user feedback. It often arrives in large volumes, is scattered across platforms, and requires significant manual effort to categorize and prioritize. This inefficiency delays critical improvements, leaving users frustrated and businesses lagging behind.
Enter FeedSense.ai
FeedSense.ai was designed to address these pain points by providing:
- Centralized Feedback Collection: Businesses can gather all feedback in one place through customizable forms.
- AI-Powered Insights: Automatically analyze, categorize, and prioritize feedback.
- Actionable Tasks: Convert insights into tasks that can be directly integrated into development workflows.
Why I Chose Llama 2 AI
After exploring several AI models, I decided to use Llama 2 due to its:
- Advanced Natural Language Processing (NLP): Llama 2 excels at understanding the context and sentiment behind user inputs.
- Customizability: It allows fine-tuning for specific use cases, like feedback analysis.
- Efficiency: The model processes large volumes of text quickly, making it ideal for real-time feedback analysis.
The Impact So Far
- Reduced the time spent on feedback analysis by 70%.
- Improved task prioritization, helping businesses act on critical issues faster.
- Enabled teams to focus on building better user experiences instead of sifting through feedback manually.
If you’re interested in trying FeedSense.ai or learning more about how it works, feel free to reach out or visit the website. Let’s turn feedback into growth opportunities together!
Author Of article : ch rahul Read full article
