Principle:Explodinggradients Ragas Evaluation Dataset Preparation
| Knowledge Sources | Domains | Last Updated |
|---|---|---|
| explodinggradients/ragas | LLM Evaluation, Data Management | 2026-02-10 |
Overview
Evaluation Dataset Preparation is the principle of structuring evaluation data into typed, backend-agnostic containers that decouple data management from evaluation logic, enabling consistent and reproducible LLM evaluation workflows.
Description
When evaluating Large Language Model applications, the quality and organization of evaluation data directly impacts the reliability of results. Evaluation Dataset Preparation addresses this by providing a structured approach to managing evaluation data that enforces several key properties:
Typed Schema Enforcement: Evaluation datasets can optionally be bound to Pydantic data models, ensuring that every row conforms to a consistent schema. This prevents data inconsistencies that could silently corrupt evaluation results. When a data model is provided, all entries are validated against it at insertion time. When no model is provided, the dataset operates in a flexible dictionary mode suitable for exploratory work.
Backend Abstraction: The storage format for evaluation data is decoupled from the dataset interface through a backend abstraction layer. This means the same dataset API works identically whether data is stored as local CSV files, JSONL documents, in-memory structures, or remote services. Users specify a backend by name (such as "local/csv") or by passing a pre-configured backend instance. A registry system resolves backend names to their implementing classes at runtime.
List-Like Interface: Datasets behave like Python lists, supporting iteration, indexing, length queries, and append operations. This familiar interface reduces the learning curve and allows evaluation datasets to be used directly in standard Python patterns like for-loops and list comprehensions.
Persistence Lifecycle: Datasets support explicit save() and load() operations, giving users control over when data is persisted. The reload() method refreshes the in-memory data from the backend, which is useful when datasets are modified externally or by other processes.
Usage
Use the Evaluation Dataset Preparation principle when:
- Building evaluation pipelines that need to store and retrieve test data across sessions
- Defining strict schemas for evaluation inputs (such as user queries, expected responses, and reference contexts)
- Working with multiple storage backends and wanting to switch between them without changing evaluation code
- Splitting datasets for training and validation of custom metrics via
train_test_split() - Converting evaluation data to and from pandas DataFrames for analysis
Theoretical Basis
The theoretical foundation of evaluation dataset preparation rests on the Repository Pattern from software architecture, where data access logic is abstracted behind a clean interface:
PROCEDURE prepare_evaluation_dataset(name, backend, data_model):
1. Resolve the backend:
IF backend is a string:
Look up the backend class in the registry
Instantiate the backend with any additional configuration
ELSE:
Use the provided backend instance directly
2. Initialize the dataset container:
Store the name, backend, and optional data model
Initialize an empty internal data list
3. For each data entry appended:
IF a data model is defined:
Validate the entry against the Pydantic model
Store the validated model instance
ELSE:
Accept the entry as a plain dictionary
4. On save():
Convert all entries to dictionaries (model_dump for Pydantic instances)
Delegate persistence to the backend's save method
5. On load():
Retrieve dictionary data from the backend
IF a data model is defined:
Validate and convert each dictionary to a model instance
Return a new dataset instance with the loaded data
This pattern ensures that evaluation logic never depends on storage details, and that data integrity is maintained through optional schema validation at the boundary between the application and the storage layer.