AI Sheets: Practical, No-Code Dataset Work Without Agents

Abstract / Overview

AI Sheets is a no-code, spreadsheet-style tool for building, transforming, enriching, and evaluating datasets with open AI models. It runs free as a Hugging Face Space or locally. You load tabular data, add AI-generated columns driven by prompts, refine results by editing cells and liking good ones, and export a finished dataset to the Hugging Face Hub. No agents. Only cells, prompts, and models.

Try it out here

Conceptual Background

AI Sheets preserves the familiar spreadsheet loop. Your imported cells remain human-editable. New columns are generated by prompts that reference existing columns using placeholders such as {{column_name}} . You iterate by editing outputs and marking preferred examples; on regeneration, those examples act as few-shot guidance. A toggle enables web search for columns that need up-to-date facts. The interface is optimized for quick iteration on small batches before scaling.

AI Sheets integrates with the Hugging Face ecosystem and multiple Inference Providers, so you can compare models and latency characteristics without changing your data. It can also target OpenAI-compatible endpoints for local or custom deployment patterns.

Practical Setup

• Use the hosted Space to evaluate the tool with zero installation. This is the fastest path to understand prompts, column settings, and regeneration.

• Deploy locally if you need private data handling, custom endpoints, or tighter control over providers. The project README documents setup and environment variables.

• Connect to providers through the UI. Switch models per column to test structure, accuracy, and speed on your own data.

• Optional: point AI Sheets at an OpenAI-compatible local endpoint when on-prem or air-gapped constraints apply.

Step-by-Step (Practical, No Agents)

1) Bring in data

• Import CSV, TSV, XLS, or Parquet. The UI expects at least one header and one row. For fast feedback cycles, keep the interactive session to roughly a thousand rows.

• Alternatively, describe a small dataset in natural language to generate a seed table with a few rows for exploration. Extend by dragging.

2) Add AI columns

• Click “+” to create an AI column. Choose presets such as Extract, Summarize, Translate, or write a custom instruction. Reference existing columns with {{column}} .

• Toggle “Search the web” when a task requires current facts, such as adding missing ZIP codes or URLs.

• Change the model or provider per column to compare result quality and latency across options.

3) Tighten results

• Edit any generated cell to enforce exact formatting or content policy. Like strong examples. Click Regenerate to propagate examples as few-shot guidance across the column.

• Add more cells by dragging down. Use this to fill new rows or retry errored cells quickly.

4) Compare outputs directly in the sheet

• Create multiple AI columns, one per model or prompt variant, and scan results side by side.

• Add a lightweight judge column if you want an LLM to pick a better output and state a brief reason.

5) Export when done

• Export the final table to the Hugging Face Hub. The export includes a config that captures your prompts and few-shot examples, which you can reuse for larger jobs or downstream applications.

Use Cases / Scenarios

• Prompt and model “vibe testing.” Import real prompts, add columns per model, and compare side-by-side quality with or without a judge column.

• Text cleanup and normalization. Strip punctuation, unify casing, or standardize fields by prompting over {{text}} . Regenerate after curating a few ideal examples.

• Classification and tagging. Create closed-set labels for topics, routing, or priority. Edit edge cases to steer the column, then regenerate for consistency.

• Summarization at scale. Produce one-sentence or bullet summaries for tickets, reviews, or research notes. Compare variants across models in adjacent columns.

• Enrichment with retrieval. Enable web search in a column to add missing metadata such as ZIP codes or verified links, then spot-check and refine.

• Synthetic data for privacy-constrained domains. Generate plausible personas or records, then derive secondary artifacts like emails or titles from those columns.

Limitations / Considerations

• Interactive scale. The sheet is tuned for fast iteration on small batches. Move to export and batch processing when volume grows.

• Provider variability. Context length, latency, and structure differ across providers and models. Test before committing.

• Few-shot generalization. Regeneration uses liked and edited cells, but outputs remain probabilistic. Keep examples representative.

• Retrieval accuracy. Web-enabled columns can surface stale or noisy facts. Prompt for short, verifiable outputs and validate.

• Governance. Avoid sensitive data in third-party endpoints. Prefer controlled deployments and organization-scoped access.

Diagram

Practical loop from import to export.

Conclusion

AI Sheets makes the dataset work immediately and concretely. You import data, attach prompt-driven columns, and improve results by editing cells and regenerating. You can compare models in-place, enable retrieval when facts matter, and export a clean table for sharing or scaling. The approach is simple and auditable because it stays inside a spreadsheet interface.

References: https://huggingface.co/blog/aisheets