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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": []
      }
    }
  ]
}

Documentation Index

Fetch the complete documentation index at: https://productlasso.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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": []
      }
    }
  ]
}