Systematic approaches to improve the quality of AI generation include:
- Model Selection: Priority is given to large models with parameters of 70B and above for specialized content.
- Tip Engineering: Adoption of binding prompts such as 'write {{background information}} for professional emails'
- Few-shot optimization: Manually edit 3-5 ideal outputs and then click like, click to regenerate the application mode
- data validation: Create 'Professionalism Assessment' column with LLM auto-scoring
- iterative cycle: Adjust model/provider combinations based on evaluation results
- Batch Generation: Extending Quality Configurations to Generate Massive Amounts of Data with HF Jobs
With this methodology, a legal tech company increased the accuracy of its contract clause generation from 62% to 89%, greatly reducing the manual revision workload.
This answer comes from the articleAI Sheets: building and processing datasets using AI models in tables without codeThe
May not be reproduced without permission:AI productivity tools " How to optimize the professionalism and accuracy of AI-generated content?
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