Introduction to Argilla

Argilla is an open-source platform for collecting, curating, and annotating data for natural language processing and large language model workflows. It is designed to make human feedback collection a structured, trackable part of the ML pipeline rather than an ad hoc spreadsheet exercise.

Core Concepts

Datasets

An Argilla dataset defines a schema of fields (the content to review, such as a prompt-response pair) and questions (what annotators should provide, such as a rating, a label, or free-text feedback).

Records

Each record is one item to annotate — for example, a single LLM response paired with its prompt — plus any existing metadata like model name or generation parameters.

Responses

A response is an annotator’s answer to a record’s questions. Argilla tracks responses per-user, which supports both single-annotator workflows and multi-annotator agreement analysis.

Getting Started

Install and Run

pip install argilla
docker run -d --name argilla -p 6900:6900 argilla/argilla-quickstart

Python Client

import argilla as rg

client = rg.Argilla(api_url="http://localhost:6900", api_key="argilla.apikey")

settings = rg.Settings(
    fields=[rg.TextField(name="prompt"), rg.TextField(name="response")],
    questions=[
        rg.RatingQuestion(name="quality", values=[1, 2, 3, 4, 5]),
        rg.TextQuestion(name="feedback", required=False),
    ],
)

dataset = rg.Dataset(name="llm-response-review", settings=settings)
dataset.create()

dataset.records.log([
    {"prompt": "Explain gradient descent.", "response": "Gradient descent is..."},
])

Human-in-the-Loop Review Workflows

Argilla’s primary use case is structuring the review of model outputs by human annotators. Teams typically log a batch of generated responses, assign them to reviewers through the web UI, and collect ratings or corrections that feed back into evaluation or fine-tuning datasets.

model generates responses -> logged to Argilla -> human review/rating -> curated dataset -> fine-tuning or eval

Weak Supervision and Active Learning

Argilla supports semi-automated labeling through rule-based weak supervision and active learning loops, where a model’s uncertain predictions are prioritized for human review — concentrating annotator effort on the examples that will most improve the dataset, rather than reviewing everything uniformly.

Feedback Datasets for RLHF and DPO

Because Argilla natively supports comparison-style questions (ranking or preferring one of several responses), it is commonly used to build preference datasets for RLHF or DPO-style fine-tuning, where each record presents multiple candidate responses and an annotator selects the preferred one.

Argilla vs. Other Annotation Tools

Common Use Cases

• Response quality review: collecting human ratings on generated text before it’s used for fine-tuning.
• Preference data collection: building RLHF/DPO datasets by comparing candidate responses.
• Dataset curation: filtering and correcting noisy or synthetic datasets before training.
• Evaluation set construction: curating a high-quality, human-verified benchmark set from model outputs.

Argilla’s focus on structured, trackable human feedback makes it a natural fit for teams building fine-tuning or evaluation datasets that require reliable human judgment rather than fully automated labeling.