How do I preprocess data before sending it to OpenAI models?

Preprocessing data for OpenAI models involves three key steps: cleaning your data, structuring it appropriately, and formatting it for API compatibility. First, ensure your data is free of noise, errors, or irrelevant information. For example, if you’re processing text, remove duplicate entries, correct spelling mistakes, or filter out sensitive information like personally identifiable data. If working with numerical data, handle missing values by imputing averages or dropping incomplete records. The goal is to provide the model with clear, consistent inputs to improve accuracy and reduce confusion during inference.

Next, structure the data to align with the model’s requirements. For text-based models like GPT, this might involve breaking long documents into smaller chunks to fit token limits (e.g., 4,096 tokens for GPT-4). For instance, a 10,000-word article could be split into sections of 500-1,000 words each. If using embeddings, ensure inputs are semantically meaningful—like separating paragraphs by topic. For fine-tuning, organize data into prompt-completion pairs, such as formatting customer service interactions into “user query” and “agent response” entries. Proper structuring helps the model recognize patterns and generate relevant outputs.

Finally, format the data according to OpenAI’s API specifications. Most endpoints require JSON inputs with specific keys—for example, the chat completions API uses a messages array containing role (e.g., “user”) and content (the actual text). Validate data types: strings for text, numerical values for embeddings. Test edge cases, like handling special characters or emojis by ensuring proper UTF-8 encoding. If using batch processing, verify that arrays of inputs are correctly nested. Tools like Python’s json library or validation scripts can automate checks. By following these steps, you minimize API errors and ensure the model processes your data efficiently.