As large language models (LLM) become increasingly integral to applications ranging from chatbots to code generation, optimizing token usage has become a priority for developers. Efficient token management not only reduces costs but also enhances performance and user experience. Both Anthropic's Claude and OpenAI have introduced features aimed at helping developers save on token usage. In this blog post, we'll explore three innovative methods: BatchPrediction, Predicted Outputs, and Prompt Caching.
Let's take a look at these features.
1. Batch Processing
Claude's Message Batches API (Beta)
Anthropic's Message Batches API allows developers to process large volumes of message requests asynchronously. By batching multiple requests together, you can reduce costs by up to 50% while increasing throughput.
How does it work?
- Batch Creation: Create a batch containing multiple message requests.
- Asynchronous Processing: Each request in the batch is processed independently.
- Result Retrieval: Retrieve results once the entire batch has been processed.
Code Snippet: Creating a Message Batch in Python
Read more in my Blog Post
3 Ways to Save Big on Token usage in Claude and OpenAI
Both Anthropic’s Claude and OpenAI have introduced features aimed at helping developers save on token usage. In this blog post, we’ll explore three innovative methods: Batch Prediction, Predicted Outputs, and Prompt Caching
airabbit.blog
Top comments (0)