Ragas Eval Loop for Scout Loops

What It Is

Ragas is an open-source evaluation toolkit for LLM applications. Instead of reading a few generated outputs and deciding by taste, you create a small evaluation dataset, choose metrics, run an experiment, and inspect scores and failures.

In this loop, the concrete thing you will touch is a tiny Python evaluation file for Scout Loop-style outputs. You will model two generated loop responses as evaluation rows, score whether each response addresses the prompt, and check whether its claims stay supported by the supplied source context.

Why It Matters

For AI, data, and consulting work, the leverage is not just knowing that Ragas exists. The useful question is whether it can turn subjective review into a repeatable quality signal you can explain to a client, teammate, or future product user.

Scout Loops are a good test case because they need both fit and groundedness. A generated loop can sound fluent while missing the user's topic, overclaiming from sources, or burying the first real action. A small Ragas experiment gives you evidence about whether the tooling helps catch those failures before you invest in a larger eval harness.

Guided First Play

You are going to build one minimal eval run: two Scout Loop candidate responses, one short source context, and two metrics. By the end, you should know whether Ragas feels practical enough for a deeper Scout Loop quality system.

Create the smallest runnable workspace

Open a terminal in a scratch folder and create an isolated Python environment. If you already have a preferred environment manager, use it; the goal is simply to avoid mixing eval dependencies into an unrelated project.

mkdir ragas-scout-eval
cd ragas-scout-eval
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install ragas langchain-openai datasets pandas

Ragas metrics that use an evaluator LLM need a model provider key. For this first pass, use a tiny dataset so cost and runtime stay bounded.

export OPENAI_API_KEY="your_key_here"

Write a two-row evaluation file

Create eval_scout_loops.py. The first row is a decent candidate: it answers the prompt and stays close to the context. The second row is intentionally weak: it talks around the topic and invents detail not present in the context. That contrast makes the verification step meaningful.

import pandas as pd
from datasets import Dataset
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

rows = [
    {
        "question": "Create a 40-minute Scout Loop for using Ragas to evaluate generated Scout Loops.",
        "answer": "Build a tiny Ragas eval with two generated Scout Loop candidates, a short source context, answer relevancy, and faithfulness. Use the scores to decide whether Ragas is worth a deeper evaluation pipeline.",
        "contexts": [
            "Ragas supports evaluation datasets, metrics, and experiments for LLM applications. Its available metrics include answer relevancy and faithfulness. Faithfulness checks whether response claims are supported by context."
        ],
    },
    {
        "question": "Create a 40-minute Scout Loop for using Ragas to evaluate generated Scout Loops.",
        "answer": "Start by building a full production dashboard with user accounts, tracing, billing, and a month of synthetic traffic. Ragas automatically proves the product is correct.",
        "contexts": [
            "Ragas supports evaluation datasets, metrics, and experiments for LLM applications. Its available metrics include answer relevancy and faithfulness. Faithfulness checks whether response claims are supported by context."
        ],
    },
]

dataset = Dataset.from_list(rows)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

result = evaluate(
    dataset,
    metrics=[answer_relevancy, faithfulness],
    llm=llm,
    embeddings=embeddings,
    raise_exceptions=False,
)

scores = result.to_pandas()
print(scores[["question", "answer_relevancy", "faithfulness"]])
print("\nRows with missing metric values:")
print(scores[scores[["answer_relevancy", "faithfulness"]].isna().any(axis=1)])

Run it and inspect the signal

python eval_scout_loops.py

Do not overread the exact numbers from a two-row dataset. The first useful signal is directional: the grounded candidate should look better than the overbuilt, overclaiming candidate. If both rows score similarly, that is still useful evidence: either the metric choice, dataset shape, evaluator model, or prompt representation is not yet discriminating enough.

Try one focused variation

Change only the second answer. Keep the same question and context, but make the bad answer topically relevant while still unsupported by the context.

"Use Ragas topic adherence, tool-call F1, and agent goal accuracy to grade every Scout Loop. The official quickstart says these are the default metrics for this use case."

Fallback if API access blocks the run

If you cannot run evaluator-backed metrics right now, still finish the Scout Loop by scoring the same two rows manually. Give each row a 0, 0.5, or 1 for these two checks: does it address the requested Scout Loop, and are its concrete claims supported by the context? If that manual rubric already feels valuable, Ragas is likely worth testing with real metrics later. If even this tiny rubric feels unrelated to your work, stop here.

Verification

• You can point to the exact evaluation rows being scored.
• You can explain why answer relevancy and faithfulness test different failure modes.
• You inspected missing metric values instead of treating the table as automatically valid.
• You have one concrete opinion about whether Ragas would help evaluate Scout Loop generation beyond manual review.

Your Next Move

Choose based on the evidence you just created: did Ragas give you a useful quality signal for generated Scout Loops, or did the setup and metric behavior feel heavier than the value?

Go Deeper would turn this into a 2-hour eval design pass with 10 to 20 real Scout Loop examples, clearer pass-fail labels, and metric comparison. Project would mean building a reusable evaluation harness for generated learning loops, including dataset storage, experiment names, failure review, and a small reporting view.

Sources

This loop is grounded in the Ragas official documentation and primary repository: the introduction and quickstart for the overall workflow, evaluation dataset and schema references for the row shape, the available metrics pages for answer relevancy and faithfulness, and the evaluate() reference for running metrics over a dataset. Testset generation and migration notes informed what to leave for a deeper loop rather than crowding this first pass.