Batch AI Processor — Run a Prompt Over Every CSV Row Locally

The Batch AI Processor is designed for privacy and performance. Instead of sending your sensitive CSV data to an external API, we run the entire processing pipeline locally within your browser using a powerful local Large Language Model (LLM). When you upload your spreadsheet—for example, 5,000 rows of customer reviews—you define a single prompt, such as 'Extract the primary emotion and rating (1-5)'.

The tool then iterates through every row sequentially. For each record, it inputs the data into the local LLM instance, which generates the required output field directly in your session memory. This process ensures that your raw data never leaves your computer, providing a secure and efficient way to classify or extract information at scale.

• Local Processing: Runs the AI engine client-side.
• Batch Execution: Processes data row by row efficiently.
• Data Integrity: Maintains full control over your uploaded dataset.

To maximize the accuracy and consistency of your batch processing, structure your prompt using Role-Playing and few-shot examples. Tell the AI exactly who it is and what format to use.

For example: 'You are a financial analyst. Your task is to classify investment risk (Low, Medium, High). Here is an example of good input/output: Input: [Text] | Output: [Risk Level]. Now process the following rows.' This vastly improves adherence to constraints.

• Use Delimiters in Prompts: Use triple backticks () or clear headers within your prompt to separate instructions from the actual data fields.
• Define Output Schema: Explicitly state that you want JSON output with specific keys (e.g., {'sentiment': '...', 'keywords': [...] }). This is essential for easy post-processing.
• Chunking Large Datasets: If your CSV exceeds memory limits, consider splitting it into logical chunks (e.g., quarterly reports) to ensure stable processing performance.