Use cases
Debugging – Identify which inputs drove unexpected or incorrect outputs. This helps locate problematic data sources or training examples. Auditing – Document how AI systems reach decisions. Explainability provides traceable links between outputs and source materials for compliance and verification. Dataset refinement – Analyze influential inputs to identify gaps or issues in your retrieval sources or training data.Attribution capabilities
SeekrFlow provides two attribution capabilities for different workflows:Context attribution
Identifies which retrieved sources influenced an agent’s response. Traces statements back to vector database content, web search results, tool outputs, and conversation context.When to use: Agents with RAG, web search, or tools.
Training data attribution
Identifies which training examples influenced a fine-tuned model’s response. Traces outputs back to specific question-answer pairs from the training dataset.When to use: Fine-tuned models.
Source tracing
Identifies the exact location in the source document that a retrieved chunk came from. Traces responses back to the specific file, page, and lines the agent retrieved.When to use: Agents with RAG where document-level proof of origin is required.
Choose the right capability
| Workflow | Attribution type | What it traces |
|---|---|---|
| Agents with RAG, web search, or tools | context attribution | Retrieved sources that influenced each statement |
| Fine-tuned models | training data attribution | Training examples that shaped model behavior |
| Agents with RAG | source tracing | Exact location in the source document for each retrieved chunk |