Incorporating User Feedback into RAG Evaluation Datasets To build a RAG (Retrieval-Augmented Generation) evaluation dataset using real user feedback or queries, start by collecting raw data from user interactions. This includes logging queries from live systems, customer support tickets, or public forums where users ask questions. For example, a customer service chatbot’s logs can provide diverse, context-rich queries. Next, anonymize and clean the data to remove personally identifiable information (PII) and irrelevant noise. Then, pair each query with a "ground truth" answer, which can be derived from verified responses (e.g., expert-provided answers in support systems) or aggregated user corrections. For instance, if users frequently edit a chatbot’s response to a query, the revised answer could serve as the target for evaluation. Finally, structure the dataset to include metadata like timestamps, user intent categories, or query complexity to enable nuanced evaluation of retrieval and generation performance.
Challenges of Using Real-World Queries Real-world queries introduce variability and ambiguity that synthetic datasets lack. Users often phrase questions informally (e.g., "How 2 fix error 404?"), use domain-specific jargon, or omit context, making it hard for RAG systems to parse intent. For example, a query like "It’s not working" lacks specificity, challenging the system to infer the issue. Privacy is another hurdle: even anonymized data may retain patterns that risk re-identification. Additionally, real-world data often reflects biases—e.g., overrepresentation of certain demographics—which can skew evaluation metrics. For example, a dataset dominated by tech-related queries might not test a RAG system’s ability to handle medical or legal terminology. Lastly, creating ground truth answers at scale is labor-intensive, as human annotators are needed to validate responses or resolve conflicting user feedback.
Balancing Practicality and Quality Automating parts of the process (e.g., clustering similar queries or using weak supervision to label data) can reduce costs but risks introducing errors. For example, automated intent classification might miscategorize a query like "Why is my app crashing?" as a "technical issue" instead of a "billing problem" if contextual clues are missed. Another challenge is ensuring the dataset covers edge cases and failure modes. Real-world data may lack rare but critical scenarios (e.g., handling multilingual queries) unless explicitly sampled. To address this, teams can augment the dataset with adversarial examples derived from user feedback, such as paraphrased queries that previously caused errors. However, maintaining a balance between real-world representativeness and evaluation rigor remains a key trade-off, as overly niche queries might not reflect typical usage patterns.