llms.txt The core mechanisms for improving the efficiency of AI information processing are embodied in three dimensions:
- Structured load shedding: Provide cleaned and pure data for AI, eliminating computationally intensive tasks such as html parsing, ad filtering, dynamic content rendering, etc., which is estimated to reduce preprocessing overhead by more than 601 TP3T
- semantic enhancement: Standardized ways of organizing and describing information (e.g., the structure of background notes → guidance content → detailed links) dramatically improve LLM comprehension accuracy
- Search Optimization: similar to AI-specific SEO solutions, /llms-full.txt indexes all documents centrally, helping the tool to quickly locate relevant information
Actual cases show that the document system using llms.txt can make ChatGPT and other general LLM answer accuracy increased by 47%, response speed increased by 35%. Especially solved the problem of search failure caused by inconsistency of terminology in the traditional document retrieval (e.g. "invoice editing" vs. "invoice editing" vs. "billing adjustment") in traditional document searches, resulting in search failures.
This answer comes from the articlellms.txt: Standardized Site Information Documentation for Large Language ModelsThe
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