Workflow solutions for building automated content production lines
For self-media operations, marketing copywriting and other scenarios that require large-scale content output, they can be optimized in the following ways:
- Template generation: Design the prompt as a template with replaceable variables, for example:
"生成5条关于{产品}的微博文案，风格：{风格}，长度不超过140字" - Concurrent request optimization::
1. Using asynchronous requests (python's aiohttp)
2. Set reasonable rate limits (10req/s initially recommended)
3. EnablingstreamPatterns acquire real-time responses - Quality filtering mechanisms::
1. AdoptionlogprobsParameter Get Generation Confidence
2. Setting up content duplication detection (e.g. ROUGE scoring)
3. Establishment of keyword blacklist filters
Typical workflow:
1. Generate an outline with Gemma → Llama 3 Expanded Content → Mistral for stylized touch-ups
2. AdoptionseedParameters maintain generation consistency
3. Automatic optimization of prompts with AutoML tools
This answer comes from the articleDeepInfra Chat: experiencing and invoking a variety of open source big model chat servicesThe
May not be reproduced without permission:AI productivity tools " How to apply DeepInfra for efficient batch generation in content creation scenarios?
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