A useful but little-known OpenAI API feature 📦 Sending thousands of LLM requests one by one and wincing at the bill? There's a much cheaper way. OpenAI's "Batch" API lets you bundle requests together for async execution at a significant discount. It's the go-to for evaluations, classification, data generation, and any high-volume job that doesn't need real-time responses. 📌 Title: Batch 🔗 URL: 🧩 Overview When you're calling the LLM at scale, sending requests one at a time is expensive and slow. The Batch API lets you upload requests as a JSONL file, process them all asynchronously, and get results at a steep discount compared to standard API calls. Results are collected once the batch completes. 🛠 How to use it Compile your requests into a JSONL file, upload it, and create a batch job. When processing finishes, download the results file. Each request uses the same Chat Completions format you already know, so existing prompts work as-is. Pair with Webhooks to get notified automatically when a batch is done. 🏗 Building it into production ・Dataset classification and labeling: run tens of thousands of text categorizations as an overnight batch. Labeled data is ready by morning. ・Synthetic data generation pipelines: when you're generating training data at scale, the batch discount makes a material difference to your bill. ・Model evaluation and benchmarking: run quality comparisons across multiple prompts in one shot. Analyze results together. ・Periodic summarization and reporting: weekly article digests, customer feedback analysis, anything that processes in bulk on a schedule. 💡 Use cases 🗂 Large-scale text classification and tagging 🧬 Synthetic and training data generation 📊 Model evaluation and prompt comparison 📝 Scheduled batch summarization and extraction ⚠️ Watch out Batch processing is async, so results take time to come back. Not suitable for anything that needs a real-time response. Individual requests within a batch can also fail, so build proper error handling when parsing the results file. Start with a small test batch before submitting massive jobs. ✨ The foundation of cost optimization at scale is batching. Switch your evaluation pipeline to Batch first and see the difference on your next invoice. #OpenAI# #LLM#