This Langflow flow creates an automated evaluation system for AI assistant responses. The flow takes a user question and an AI agent's response, then uses a language model to evaluate the quality of that response against reference documents. It produces a structured evaluation report with detailed scoring and reasoning.

The evaluation process begins when a user provides both a question and an AI agent's response through a chat input component. This input is then processed by an AI agent that has access to reference documents through a file reading tool. The agent uses a detailed prompt that establishes it as an expert evaluator with specific instructions for analyzing responses based solely on the provided reference materials.

The agent's evaluation is then converted into structured data using a language model component that formats the assessment according to a predefined schema. This structured output includes four key evaluation criteria: accuracy, reference alignment, clarity, and omissions/hallucinations. Each criterion receives a numerical score and detailed reasoning. Finally, a parser component transforms this structured data into a formatted evaluation report that presents the scores and explanations in a clear, readable format for review.

You can implement this evaluation system using either the integrated Cleanlab Evaluator for automated trust scoring or build a custom judge using structured output to define your own scoring schema. The system can route responses based on score thresholds using If-Else components, automatically blocking low-quality responses or flagging them for human review.

The flow can be extended using other Langflow nodes to create more sophisticated evaluation pipelines. You can integrate Langfuse for observability to track evaluation metrics over time, use API Request nodes to send results to external monitoring systems, or trigger the entire evaluation process through webhooks from your production applications. Multiple evaluation criteria can be implemented in parallel, with different scoring models for accuracy, tone, and compliance, all feeding into a final decision matrix.