Decision attribution traces every AI output back to its contributing inputs: which documents were retrieved, what context was assembled, and which factors influenced the response. It transforms debugging from guesswork into targeted investigation. For businesses, this means fixing the right problems instead of blaming the wrong components. Without it, AI failures remain mysteries.
The AI gave the wrong answer. Everyone agrees something went wrong.
Was it bad data? Wrong context? Model limitations? Prompt issues?
Without attribution, you fix the wrong thing and the problem returns.
You cannot fix what you cannot trace. Attribution connects every AI output to its causes.
QUALITY LAYER - Makes AI decisions traceable from output back to inputs.
Decision attribution records the complete chain of causation for every AI output: which documents were retrieved, what context was assembled, which prompt was used, and what factors influenced the final response. When something goes wrong, you can trace backward from the output to identify exactly what contributed.
Good attribution goes beyond logging. It creates explicit links between the AI output and each input that shaped it. You can ask: "Which retrieved document contributed to this claim?" or "What part of the system prompt caused this behavior?" and get specific, actionable answers.
AI systems fail in complex ways. Attribution transforms "something went wrong" into "this specific input caused this specific output." That specificity is what makes problems fixable.
Decision attribution solves a universal problem: how do you trace an outcome back to its causes? The same pattern appears anywhere you need to understand why something happened.
Link every output to its contributing inputs. Preserve enough context to reconstruct the decision path. Make the chain traversable in both directions.
This AI made a product recommendation. Enable attribution to see which inputs influenced each part of the output, then click any claim to trace it.
Connect outputs to their inputs
Record which retrieved documents, context items, and prompt elements contributed to each AI response. Store explicit references that can be followed later.
Measure contribution weight
Use attention weights, retrieval scores, or LLM self-assessment to estimate how much each input actually influenced the output. Rank inputs by their contribution.
Capture the decision logic
Use chain-of-thought prompting or similar techniques to have the AI explain its reasoning. Store these explanations alongside outputs for later analysis.
Answer a few questions to get a recommendation tailored to your situation.
How often do you need to debug AI outputs?
A customer complains about a bad product recommendation. With decision attribution, the support lead traces backward from the output: which documents were retrieved, what context was assembled, and which factors influenced the recommendation. The root cause becomes visible.
Hover over any component to see what it does and why it's neededTap any component to see what it does and why it's needed
Animated lines show direct connections · Hover for detailsTap for details · Click to learn more
This component works the same way across every business. Explore how it applies to different situations.
Notice how the core pattern remains consistent while the specific details change
You record that 5 documents were in the context, but the AI only used one. When the output is wrong, you waste time investigating documents that had no effect on the result.
Instead: Add influence scoring. Use attention weights, retrieval scores, or explicit citation tracking to identify which inputs actually shaped the output.
You update the prompt template or retrieval system. Now old attribution data points to versions that no longer exist. Historical debugging becomes impossible.
Instead: Version everything. Store references to specific versions of prompts, retrievers, and models. Keep old versions accessible for debugging historical issues.
You record attribution information, but finding "all outputs influenced by this document" requires manually searching through thousands of records.
Instead: Build queryable indexes. Store attribution as structured data with reverse lookups. You should be able to go from input to outputs as easily as from output to inputs.
Decision attribution is the practice of linking AI outputs to their contributing inputs. It records which documents, context items, and prompt elements influenced each response. When something goes wrong, you can trace backward from the output to identify exactly which input caused the problem, enabling targeted fixes instead of guesswork.
AI systems combine many inputs in complex ways. Without attribution, debugging is guesswork. You might fix the prompt when the real problem was bad retrieved data. Attribution provides the evidence chain needed to identify root causes, prove compliance, and improve systems systematically.
Logging captures what happened. Attribution explains why. Logs record that document X was in the context. Attribution shows that document X influenced claim Y in the output. Logs are the raw data. Attribution organizes that data into traceable chains from outputs to causes.
Three main approaches exist. Input-output linking records which inputs were present for each output. Influence scoring uses attention weights or retrieval scores to estimate how much each input affected the output. Reasoning chains use chain-of-thought prompting to capture the AI explanation of its logic.
Implement attribution when you need to debug AI outputs regularly, prove why decisions were made for compliance, or identify patterns in AI behavior. Start simple with input-output linking. Add influence scoring when debugging frequency increases. Add reasoning chains when accountability is required.
Have a different question? Let's talk
Choose the path that matches your current situation
You have no visibility into why AI made specific decisions
You log inputs and outputs but debugging is still slow
You have attribution data but want deeper insights
You have learned how to trace AI decisions back to their sources. The natural next step is using that traceability to systematically evaluate AI quality.