Introduction

LastMile is the full-stack developer platform to debug, evaluate and improve LLM applications. We make it easy to fine-tune custom evaluators, set up guardrails & monitor app performance.

Developer quickstart

Compute your first evaluation metric within 5 minutes.

python

from lastmile.lib.auto_eval import AutoEval, Metric
import pandas as pd

result = AutoEval().evaluate_data(
  data=pd.DataFrame({
      "input": ["Where did the author grow up?"],
      "output": ["France"],
      "ground_truth": ["England"]
  }),
  metrics=[Metric(name="Faithfulness")]
)

print(f'Evlauation result:', result)

node.js

import {Lastmile, Metric} from 'lastmile';

const client = new Lastmile();

const response = await client.evaluation.evaluate({
  input: ["Where did the author grow up?"],
  output: ["France"],
  groundTruth: ["England"]
  metric: Metric(name: "Faithfulness")
});

Design your own metric

Use the fine-tuning service to design your own evaluators that represent custom criteria for your app quality.

1. Create Datasets>

Upload and manage application data for running and training evals, and generate synthetic labels.

2. LLM Judge Active Labeling>

Generate high-quality labels for your data using LLM Judge with human-in-the-loop

3. Fine-tune Models>

Use the AutoEval fine-tuning service to develop custom metrics for your application.

4. Run Evals>

Compute metrics by running high-performance inference on a prebuilt or fine-tuned model.

Out-of-the-box metrics

Batteries-included evaluation metrics covering common AI application types, such as RAG and multi-agent compound AI systems.

Faithfulness>

Measures how adherent or faithful an LLM response is to the provided context. Often used for hallucination detection.

Semantic Similarity>

Measures semantic similarity between two strings. Often used for context relevance, or input/output relevance, or similarity between a response and ground truth.

Summarization Quality>

Quantify the quality of a summarization response.

Toxicity>

Quantify the toxicity level in an LLM response.

More>

Explore other metrics available in AutoEval, or keep reading to design your own metric.

Meet alBERTa 🍁

alBERTa is a family of small language models (SLMs) designed for evaluation. They are optimized to be:

• small -- 400M parameter entailment model
• fast -- can run inference on CPU in < 300ms
• customizable -- fine-tune for custom evaluation tasks

alBERTa-512 🍁>

2048 token context, specialized for evaluation tasks (like faithfulness), and gives a numeric 0->1 score.

alBERTa-LC-8k 🍁>

Long-context window variant that can scale to 128k+ tokens using a scaled dot-product attention layer

Explore our guides

Quickstart>

Start-to-finish overview of AutoEval, from running evals, labeling with LLM Judge to fine-tuning a custom metric.

Retrieval systems>

Evaluate a RAG application for hallucination, relevance and other out-of-the-box metrics available via AutoEval.

Real-time guardrails>

Build real-time guardrails in a RAG application using fine-tuned alBERTa 🍁 models.