This content has been translated from Japanese using LLM. There may be some awkward nuances in the translation.
Query History
Query history is a feature that saves search terms. This feature enables efficient searching and continuous learning.
Why Query History is Necessary
Characteristics of Search in the LLM Era
In the LLM era, it is desirable to frequently re-search while slightly changing the nuance of the search terms.
Important point: Since search results and information obtained can change significantly just by slightly modifying prompts, a multi-faceted approach becomes important.
Need for Continuous Exploration
Exploring from different angles to gain new information is essential in modern information gathering. Query history is designed to support this continuous exploration process.
Query History Features
Saving Search Terms
Query history saves search terms and records which sites were searched simultaneously.
This makes it possible to reproduce the same search conditions later.
Analysis of Search Patterns
By analyzing saved search history, you can understand:
- Patterns of effective search terms
- Frequency and trends of searches
- Progress of information gathering
Effective Utilization Methods
Gradual Search Improvement
- Initial search: Start with basic search terms
- Adjustment of search terms: Fine-tune search terms while viewing results
- Recording patterns: Save effective search patterns
Optimization of Learning Process
By utilizing query history, you can optimize the process of continuous learning and discovery:
- Comparison with past search results
- Improvement of search strategies
- Efficiency of information gathering
Technical Features
Automatic Saving Function
Each time a search is executed, the following information is automatically saved:
- Search terms
- Target sites for search
- Search date and time
- Summary of search results
Flexible Management
Saved history can be managed by:
- Filtering by search terms
- Sorting by date
- Analysis by frequency
Summary
Query history is a feature that supports efficient searching in the LLM era. By saving and reproducing search terms, it enables exploration from different angles and realizes broader information gathering. This feature optimizes the process of continuous learning and discovery, maximizing the effectiveness of parallel search.