Feedback Intelligence
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About
Feedback Intelligence is a product analytics tool for Large Language Model (LLM) applications like chatbots and voice agents. It analyzes user interactions (implicit feedback) to identify user intent, debug bad responses, and provide actionable recommendations for improvement. Unlike traditional analytics, FI focuses on understanding *why* users behave a certain way, rather than just *what* they do. It helps to improve prompts, queries, and the knowledge base. It also offers synthetic data generation and augmentation capabilities. The platform integrates with existing LLM stacks via SDKs or data uploads.
Platform
Task
Features
• interaction analytics
• synthetic data generation and augmentation
• llm app optimization
• bad response analysis
• implicit feedback analysis
FAQs
What’s Feedback Intelligence?
Feedback Intelligence is a product analytics tool designed for LLM-based applications such as chatbots, voice agents, conversational AI. Think of it as Mixpanel or Amplitude, but tailored specifically for LLM applications.
How does Feedback Intelligence work?
Feedback Intelligence gathers and processes user interactions, converting implicit feedback into actionable insights. It uses NLP-driven analysis to detect patterns, uncover issues, and make recommendations, helping AI teams continuously optimize their LLM applications.
Who can use Feedback Intelligence?
Feedback Intelligence helps developers, data scientists, and product managers capture and analyze implicit feedback—user interactions, intent, and behavior — to diagnose and optimize app performance.
What’s the difference between explicit and implicit feedback?
Explicit feedback is directly provided by users, such as ratings or written comments. Implicit feedback is inferred from user actions, like interactions, navigation paths, or repeated queries. Both types are essential for a comprehensive understanding of how users engage with an app.
What is implicit feedback, and how does Feedback Intelligence analyze it?
Implicit feedback includes user actions and engagement patterns that provide indirect insights into user satisfaction, needs, intents, and ethics. Feedback Intelligence captures and analyzes these patterns to detect emerging issues and optimize the app based on real-world use without needing direct user input.
How do you generate or augment data?
Feedback Intelligence uses synthetic data generation and augmentation techniques with implicit feedback analytics insights to enhance model training when additional data is needed. By replicating real-world scenarios and variations, we ensure the model can perform well under diverse conditions, even with limited initial data.
How does Feedback Intelligence help identify the root cause of issues?
Feedback Intelligence uses pattern recognition and clustering algorithms to identify common themes or anomalies in feedback. It traces these patterns to their root causes, such as LLM apps limitations (eg., knowledge holes, bad propmts) or user misunderstanding, providing clear recommendations to address and prioritize fixes.
What is root cause analysis?
Root cause analysis is a method Feedback Intelligence uses to pinpoint the reason of underlying issues that affect LLM performance. By identifying the source of a problem, it helps teams resolve issues at their origin, improving overall app reliability and user experience.
Does Feedback Intelligence use predefined issue labels, or can it identify and cluster new issues over time?
Feedback Intelligence is adaptable; it can start with predefined issue labels but also dynamically identify and cluster emerging issues over time. This allows the platform to adjust as new patterns and user behaviors appear, ensuring continuous and comprehensive optimization.
How do you find issues with the outputs?
Feedback Intelligence continuously monitors and analyzes output quality based on both implicit and explicit feedback signals. It detects inconsistencies, accuracy issues, or misalignments in responses, allowing the team to address these issues proactively.
What is user satisfaction score and user intent score?
User Satisfaction Score: Measures overall user contentment with app responses, derived from feedback and engagement patterns. User Intent Score: Evaluates how well the app’s responses match user intent, helping identify misinterpretations or misalignments that need improvement.
What are the use cases and applications Feedback Intelligence works with?
Feedback Intelligence is used across applications such as virtual assistants, customer service chatbots, content generation tools, and other LLM-powered solutions in sectors like healthcare, finance, and e-commerce, where user alignment and reliability are essential.
What kinds of chatbots and LLM apps can connect with Feedback Intelligence?
Feedback Intelligence supports a range of LLM-based applications, including conversational AI, virtual assistants, and customer support bots, and integrates easily with most platforms using APIs.
Is Feedback Intelligence real-time?
Yes, Feedback Intelligence captures and processes feedback in real time, allowing teams to respond to emerging issues promptly and continuously optimize the user experience.
How can we use Feedback Intelligence?
Check out our docs <https://docs.feedbackintelligence.ai/>.
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