> ## Documentation Index
> Fetch the complete documentation index at: https://docs.promptlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Results and failures

> Read EvalResult and fail the run when scores miss your bar.

`evaluate(...)` returns an `EvalResult` with a dashboard `url`. Open that link to inspect the experiment sheet.

## When the run fails

`EvaluationFailedError` is raised when:

* Any scorer cell status is `FAILED`, or
* You set `passing_score` / `passingScore` and the overall score is missing or below that threshold

Pass bars are rates in `[0.0, 1.0]` (so `0.8` means 80%).

Set `include_failure_examples` / `includeFailureExamples` to print up to five failing rows after the summary.

<CodeGroup>
  ```python Python theme={null}
  from openai import OpenAI
  from promptlayer import evaluate, contains_scorer, EvaluationFailedError

  client = OpenAI()

  def run_llm(user_message: str) -> str:
      completion = client.chat.completions.create(
          model="gpt-5.6",
          messages=[
              {"role": "system", "content": "Answer in one short sentence."},
              {"role": "user", "content": str(user_message)},
          ],
      )
      return completion.choices[0].message.content or ""

  try:
      result = evaluate(
          "support-gate",
          dataset=[{"input": "How do I reset my password?"}],
          runner=run_llm,
          scorers=[contains_scorer(source="Output", value="reset")],
          passing_score=0.8,
          include_failure_examples=True,
      )
      print(result["url"])
  except EvaluationFailedError as exc:
      print(exc.failing_row_indices)
      print(exc.result["url"])
  ```

  ```javascript JavaScript theme={null}
  import OpenAI from "openai";
  import { evaluate, containsScorer, EvaluationFailedError } from "promptlayer";

  const client = new OpenAI();

  async function runLlm(userMessage) {
    const completion = await client.chat.completions.create({
      model: "gpt-5.6",
      messages: [
        { role: "system", content: "Answer in one short sentence." },
        { role: "user", content: String(userMessage) },
      ],
    });
    return completion.choices[0].message.content ?? "";
  }

  try {
    const result = await evaluate("support-gate", {
      dataset: [{ input: "How do I reset my password?" }],
      runner: runLlm,
      scorers: [containsScorer({ source: "Output", value: "reset" })],
      passingScore: 0.8,
      includeFailureExamples: true,
    });
    console.log(result.url);
  } catch (err) {
    if (err instanceof EvaluationFailedError) {
      console.log(err.failingRowIndices);
      console.log(err.result.url);
    }
    throw err;
  }
  ```
</CodeGroup>

## What is in the result

| Field              | Python                 | JavaScript           |
| ------------------ | ---------------------- | -------------------- |
| Dashboard link     | `url`                  | `url`                |
| Eval name          | `name`                 | `name`               |
| Failed row indexes | `failed_row_indices`   | `failedRowIndices`   |
| Per-scorer totals  | `score_cards`          | `scoreCards`         |
| Case count         | `total_rows`           | `totalRows`          |
| Table / sheet ids  | `table_id`, `sheet_id` | `tableId`, `sheetId` |

The result does not inline the overall aggregate score — open `url` or use Tables scorecard APIs.