# AI use cases for Ragas (vibrantlabsai/ragas)

Repo: [vibrantlabsai/ragas](https://github.com/vibrantlabsai/ragas) | Use cases: 3 | Page: https://dedupedai.com/use-cases/vibrantlabsai-ragas/

Ragas is a Python toolkit for evaluating LLM applications. It offers LLM-based and traditional metrics, automatic test data generation, integrations with frameworks like LangChain, and a `ragas quickstart` command that scaffolds ready-made evaluation projects such as a RAG evaluation template.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Scaffold a RAG evaluation project in one command: https://dedupedai.com/use-cases/vibrantlabsai-ragas/scaffold-a-rag-evaluation-project-with-ragas/
- Score LLM outputs with a custom pass or fail metric: https://dedupedai.com/use-cases/vibrantlabsai-ragas/score-llm-summaries-with-a-ragas-discrete-metric/
- Build test data and feedback loops for an LLM app: https://dedupedai.com/use-cases/vibrantlabsai-ragas/generate-a-test-dataset-for-your-llm-app-with-ragas/

## How to scaffold a RAG evaluation project in one command with Ragas

Repo: [vibrantlabsai/ragas](https://github.com/vibrantlabsai/ragas) | Difficulty: Beginner | Time: 20 minutes | Basis: From the README

Use the Ragas quickstart CLI to generate a complete example project for evaluating a RAG system.

**Who it is for:** A developer who has a RAG app and no evaluation setup yet.

**The problem:** Starting an eval harness from a blank file is slow and easy to get wrong.

**The outcome:** A ready-to-edit RAG evaluation project in a folder you choose.

### Steps

1. Install Ragas with `pip install ragas`
2. Run `ragas quickstart` to list the available templates
3. Run `ragas quickstart rag_eval -o ./my-project` to create the RAG evaluation project
4. Open the project, point it at your own RAG pipeline and run it

### Prompt to paste into your agent

```text
Using vibrantlabsai/ragas, install it with `pip install ragas`, run `ragas quickstart rag_eval -o ./my-project`, then wire the generated project to <my RAG pipeline> and explain each file it created.
```

### Caveat

Only the rag_eval template is available today; agent, benchmark, prompt and workflow templates are listed as coming soon.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/vibrantlabsai-ragas/scaffold-a-rag-evaluation-project-with-ragas/

## How to score LLM outputs with a custom pass or fail metric with Ragas

Repo: [vibrantlabsai/ragas](https://github.com/vibrantlabsai/ragas) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Define an Aspect Critique style metric with DiscreteMetric and let an LLM judge label each response.

**Who it is for:** A product engineer who wants a quick accurate or inaccurate check on generated summaries.

**The problem:** Manual review of every summary does not scale.

**The outcome:** A label and a written reason for each response you score.

### Steps

1. Install Ragas with `pip install ragas` and set your OPENAI_API_KEY environment variable
2. Create an LLM with llm_factory and an AsyncOpenAI client as in the README
3. Define a DiscreteMetric with a name, allowed_values like accurate and inaccurate, and a prompt
4. Call metric.ascore with the llm and your response, then print score.value and score.reason

### Prompt to paste into your agent

```text
With vibrantlabsai/ragas, write a script that defines a DiscreteMetric named summary_accuracy with allowed values accurate and inaccurate, scores each response in <my file of summaries> with metric.ascore, and writes value and reason to a CSV.
```

### Caveat

LLM judges can be wrong; spot check a sample by hand.

Risk, cost: Every scored response is an LLM call billed to your provider account.

Risk, credentials: The example reads your OPENAI_API_KEY from the environment; keep it out of committed code.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/vibrantlabsai-ragas/score-llm-summaries-with-a-ragas-discrete-metric/

## How to build test data and feedback loops for an LLM app with Ragas

Repo: [vibrantlabsai/ragas](https://github.com/vibrantlabsai/ragas) | Difficulty: Intermediate | Time: 1 hour | Basis: From the README

Use Ragas test data generation to cover more scenarios, then rescore after each change.

**Who it is for:** A team that has no labeled test set for its LLM feature.

**The problem:** Without a test set, every prompt change is a guess.

**The outcome:** A repeatable evaluation run you can compare before and after changes.

### Steps

1. Install Ragas with `pip install ragas`
2. Follow the Test Data Generation section of the Ragas docs to create a dataset from your own documents
3. Score your app against it with the pre-built metrics
4. Rerun after each prompt or retrieval change and compare scores

### Prompt to paste into your agent

```text
Using vibrantlabsai/ragas, generate a test dataset from <my documents folder> with its test data generation feature, score my app with the pre-built metrics, and save a baseline report I can compare against next week.
```

### Caveat

The README describes test data generation but its code lives in the linked docs.

Risk, data: Ragas collects anonymized usage data by default; set RAGAS_DO_NOT_TRACK to true to opt out.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/vibrantlabsai-ragas/generate-a-test-dataset-for-your-llm-app-with-ragas/