Inference history and feedback endpoints on Pioneer

Pioneer stores every inference call and lets you retrieve results by ID or in bulk. You can also submit correction feedback on individual inferences — this feedback signals what the model got wrong and powers Adaptive Inference, which automatically retrains your model on corrected examples from live traffic.

Endpoints

Method	Path	Description
`GET`	`/inferences`	List past inferences
`GET`	`/inferences/:id`	Get inference details
`POST`	`/inferences/:id/feedback`	Submit correction feedback

List past inferences

GET /inferences returns a paginated list of past inference calls. Use the query parameters below to filter results.

Query parameters

number

Maximum number of results to return per page.

number

Number of results to skip before returning. Use with limit to paginate through results.

string

Filter by model ID. Accepts a training job ID or a base model ID.

string

Filter by task type (e.g. ner, classification, generate).

string

Filter by project ID to see only inferences scoped to a specific project.

string

Filter by training job ID to see only inferences run against a specific fine-tuned model.

Example

curl "https://api.pioneer.ai/inferences?limit=20&offset=0&model_id=job_abc123" \
  -H "X-API-Key: YOUR_API_KEY"

import requests

response = requests.get(
    "https://api.pioneer.ai/inferences",
    headers={"X-API-Key": "YOUR_API_KEY"},
    params={
        "limit": 20,
        "offset": 0,
        "model_id": "job_abc123"
    }
)

print(response.json())

Get inference details

GET /inferences/:id returns the full record for a single past inference, including the input text, schema, model response, and timestamp.

curl https://api.pioneer.ai/inferences/INFERENCE_ID \
  -H "X-API-Key: YOUR_API_KEY"

import requests

response = requests.get(
    "https://api.pioneer.ai/inferences/INFERENCE_ID",
    headers={"X-API-Key": "YOUR_API_KEY"}
)

print(response.json())

Submit correction feedback

POST /inferences/:id/feedback lets you submit a corrected version of what the model should have predicted. Corrections are used as labeled training examples for Adaptive Inference.

Feedback submitted here powers Adaptive Inference — Pioneer’s continuous improvement loop that automatically retrains your model on corrections collected from live traffic. See the Adaptive Inference guide for details on how this works.

Request parameters

object

required

The corrected output. The shape should match the schema used in the original inference — for example, a corrected entities list for an NER inference.

Example

curl -X POST https://api.pioneer.ai/inferences/INFERENCE_ID/feedback \
  -H "X-API-Key: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "correction": {
      "entities": [
        {"text": "Apple", "label": "organization", "start": 0, "end": 5},
        {"text": "iPhone", "label": "product", "start": 18, "end": 24}
      ]
    }
  }'

import requests

response = requests.post(
    "https://api.pioneer.ai/inferences/INFERENCE_ID/feedback",
    headers={
        "X-API-Key": "YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "correction": {
            "entities": [
                {"text": "Apple", "label": "organization", "start": 0, "end": 5},
                {"text": "iPhone", "label": "product", "start": 18, "end": 24}
            ]
        }
    }
)

print(response.json())

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Adaptive Inference guide — continuous model improvement from live traffic

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Inference history and feedback endpoints on Pioneer

Endpoints

List past inferences

Query parameters

Example

Get inference details

Submit correction feedback

Request parameters

Example

​Endpoints

​List past inferences

​Query parameters

​Example

​Get inference details

​Submit correction feedback

​Request parameters

​Example

​Related

Endpoints

List past inferences

Query parameters

Example

Get inference details

Submit correction feedback

Request parameters

Example

Related