What is Feedback Intelligence?

Feedback Intelligence provides specialized analytics for Large Language Model (LLM) powered applications such as voice agents, chatbots, and conversational AI systems. It addresses the limitations of traditional analytics and explicit feedback, which often fail to capture the nuances of user intent or provide actionable data for improvement. The platform moves beyond simple metrics like clicks and page views, focusing instead on understanding why users interact the way they do and whether they achieve their goals.

By analyzing implicit feedback signals like failed attempts, specific keywords used, and overall success rates, Feedback Intelligence automatically extracts user intent and categorizes interactions. It identifies problematic conversations, pinpoints issues such as knowledge gaps or poorly formulated prompts and queries, and ranks these issues by impact. Based on this analysis, the tool offers concrete recommendations for optimizing the LLM application, including suggestions for refining prompts, enhancing the knowledge base, or even generating synthetic data for model fine-tuning, facilitating a continuous improvement cycle.

Features

Interaction Analytics: Analyzes implicit feedback (failed attempts, keywords, success rates) to understand user intent and satisfaction.
User Intent Extraction & Categorization: Automatically determines and groups what users want to achieve based on their interactions.
Configurable Metrics: Allows users to set up custom metrics to measure the success of their LLM application.
Bad Response Analysis: Automatically identifies and lists problematic conversations, highlighting unmet user intent, knowledge gaps, bad prompts, and bad queries.
Optimization Recommendations: Provides actionable suggestions on how to fix queries, prompts, context, knowledge base, or gather data for fine-tuning.
Synthetic Data Generation: Enables the creation of synthetic data and augmentation of datasets based on implicit feedback insights.
Integration Options: Supports integration via SDKs or direct upload of interaction data.

Use Cases

Improving chatbot performance based on user interactions.
Optimizing voice agent responses by understanding user intent.
Analyzing conversational AI effectiveness and identifying areas for improvement.
Debugging LLM application responses to reduce errors and knowledge gaps.
Measuring the success of LLM products against specific business KPIs.
Generating targeted training data for LLM fine-tuning based on real user behavior.