- Created standalone batch_llm_classifier.py for custom email queries
- Optimized all LLM prompts for caching (static instructions first, variables last)
- Configured rtx3090 vLLM endpoint (qwen3-coder-30b)
- Tested batch_size=4 optimal (100% success, 4.65 req/sec)
- Added comprehensive documentation (tools/README.md, BATCH_LLM_QUICKSTART.md)
Tool is completely separate from main ML pipeline - no interference.
Prerequisite: vLLM server must be running at rtx3090.bobai.com.au
FSSCoding
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