Performance Optimizations

## Description
Various performance optimizations could be implemented to improve the efficiency of classification operations, especially for batch processing and repeated operations.

## Tasks
- [ ] Improve batch processing efficiency with better parallelization
- [ ] Add caching mechanisms for repeated classifications
- [ ] Optimize token usage in prompts to reduce costs
- [ ] Implement request pooling for better throughput
- [ ] Add streaming support for faster initial results
- [ ] Optimize memory usage for large batch operations
- [ ] Profile and identify bottlenecks in current implementation
- [ ] Benchmark different optimization strategies

## Expected Result
A more performant library that processes classifications faster, uses fewer tokens, and handles batches more efficiently. Users should see reduced latency, costs, and resource usage.

## Why is this important?
Performance is critical, especially when processing large volumes of texts or working with strict latency requirements. Optimizations can reduce costs and improve user experience.

## Difficulty
Medium to Hard - requires careful profiling and understanding of both the codebase and LLM API behavior