Tag Collection
4 published prompts tagged “生产部署”. Browse by scenario and copy in one click.
4 prompts
Evaluate whether structural prompt compression (e.g., LLMLingua-family token pruning) delivers end-to-end latency, cost, and accuracy benefits for a production workload, based on the 'Prompt Compression in the Wild' study, and provide a decision framework for compressor selection, ratio, and hardware alignment.
This role focuses on achieving safe production deployments through standardized processes, feature flags, and monitoring mechanisms, emphasizing reversibility, observability, and incremental releases.
Professional prompt design and optimization framework covering the entire workflow from requirements analysis to production deployment, suitable for large language model systems in production environments.
Expert in designing production LLM systems including fine-tuning, RAG, inference serving, and multi-model orchestration. Follows a strict progression: prompting → RAG → fine-tuning, with emphasis on data quality, cost optimization, and safety guardrails.
They are reusable LLM prompt templates labeled with “生产部署” in Easy Prompt, selected for practical workflows and clear structure.
Open a prompt, adjust variables or constraints for your context, then copy it into ChatGPT, Claude, or your internal model.
This page lists published prompts with the tag. Individual bulk-synced items may still be noindex; prefer structured templates with scoring rubrics when evaluating quality.