Attribute a response to training data

Training data attribution surfaces the training data that influenced fine-tuned model outputs. By tracing model responses back to specific question-answer pairs from the training dataset, it helps debug model behavior and audit responses.

This method requires a fine-tuned model created with Seekr’s fine-tuning feature. Only models built after September 22nd, 2025 are supported.

Retrieve influential fine-tuning data

Endpoint: GET /v1/flow/explain/models/{model_id}/influential-finetuning-data

import os
from seekrai import SeekrFlow

client = SeekrFlow(api_key=os.environ["SEEKR_API_KEY"])

model_id = "deployment-<your-deployment-id>"

influential_data = client.explainability.get_influential_finetuning_data(
    model_id=model_id,
    question="What is SeekrFlow?"
)
print(influential_data)

model_id takes the deployment ID of a deployed fine-tuned model. A fine-tuning job ID or a model name returns a 404. To find the deployment ID, list your deployments and use the id field.

If you already have a model response from a prior chat.completions call, provide it as answer to skip an extra generation:

chat_response = client.chat.completions.create(
    model=model_id,
    messages=[{"role": "user", "content": "What is SeekrFlow?"}]
)

influential_data = client.explainability.get_influential_finetuning_data(
    model_id=model_id,
    question="What is SeekrFlow?",
    answer=chat_response.choices[0].message.content,
)

Reading the result

The response contains results, a list of influential question-answer pairs, along with the answer used (echoed back if you provided one, otherwise generated internally) and the schema version. Each result carries its id, the source file_id (use it to trace back and edit the training documents), the messages content in Q: <question>\nA: <answer> form, and an influence_level of high, medium, or low. Irrelevant pairs are filtered out and not returned. For the complete response schema, see the endpoint reference above.

Best practices

Interpreting influence levels: high means the Q/A pair strongly shaped the response; medium means moderate impact; low means minimal contribution. Look for recurring high pairs to understand the training patterns driving a response.
Unexpected results: If unrelated pairs are surfacing with high influence, review and refine your fine-tuning dataset.
Empty results: The model may simply not have found training pairs relevant to the prompt. This is not an error.

Common errors

TypeError – Raised by the SDK when a required parameter such as question is missing or invalid.
404 Not found – No deployed model matches the provided model_id. Confirm you used the deployment ID and that the deployment is active.

For all status codes, see the endpoint reference.

​Retrieve influential fine-tuning data

​Reading the result

​Best practices

​Common errors

Retrieve influential fine-tuning data

Reading the result

Best practices

Common errors