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GET
/
v1
/
tables
/
{table_id}
/
rows
/
{row_id}
/
traces
const traces = await client.enhance.traces(
  "tbl_abc123",
  "row_xyz789",
  "description"
);

for (const trace of traces.data) {
  console.log(trace.column_key);
  console.log(trace.model);
  console.log(trace.confidence);
}

{
  "data": [
    {
      "column_key": "description",
      "model": "gemini-pro",
      "confidence": 0.92,
      "request": {
        "system_prompt": "You are a product data expert...",
        "user_content": "Generate a description for: iPhone 15 Pro",
        "tools": []
      },
      "response": {
        "text": "The iPhone 15 Pro features...",
        "iterations": 1,
        "tool_calls": [],
        "thought_summary": "Used product name and specs to generate description",
        "sources": []
      }
    }
  ]
}
Traces provide full transparency into how AI generated or enhanced each column value, including the prompts, model used, confidence scores, and source references.

​
Path parameters

​
table_id
string
required
The unique identifier of the table.
​
row_id
string
required
The unique identifier of the row.

​
Query parameters

​
column_key
string
Filter traces to a specific column. Omit to get traces for all columns.

​
Response

​
data
array
const traces = await client.enhance.traces(
  "tbl_abc123",
  "row_xyz789",
  "description"
);

for (const trace of traces.data) {
  console.log(trace.column_key);
  console.log(trace.model);
  console.log(trace.confidence);
}

{
  "data": [
    {
      "column_key": "description",
      "model": "gemini-pro",
      "confidence": 0.92,
      "request": {
        "system_prompt": "You are a product data expert...",
        "user_content": "Generate a description for: iPhone 15 Pro",
        "tools": []
      },
      "response": {
        "text": "The iPhone 15 Pro features...",
        "iterations": 1,
        "tool_calls": [],
        "thought_summary": "Used product name and specs to generate description",
        "sources": []
      }
    }
  ]
}