

Promptql
Overview :
PromptQL is an AI-designed agent data access tool that retrieves data like a human by first gathering relevant information and then applying appropriate large language models (LLMs) for classification and follow-up processing, significantly improving the efficiency and accuracy of private data retrieval. This technology addresses the limitations of traditional search algorithms in closed domains, particularly when users submit non-standardized queries, allowing PromptQL to better understand and respond. The product background indicates that PromptQL aims to closely collaborate with users to build the first agent for free to assess and enhance the performance of existing AI agents/assistants.
Target Users :
Target audiences include businesses or individuals that need to manage large volumes of private data, especially those seeking to enhance efficiency and accuracy in data retrieval within closed domains. PromptQL simulates human-like data processing to yield more accurate responses for non-standardized queries, making it particularly suitable for enterprises and developers seeking highly customizable data access solutions.
Use Cases
A business uses PromptQL to retrieve Uber receipts for specific months.
A personal user queries the products they purchased in the past three months through PromptQL.
A sales team utilizes PromptQL to extract common pain points from sales opportunities.
A project manager employs PromptQL to summarize the latest activities of the Acme project and formulate action items.
Features
- Agent query planning: PromptQL simulates human cognitive processes by first gathering relevant data, followed by LLM application for classification and processing.
- Private data retrieval: Specifically designed for private data, enhancing both security and accuracy in data retrieval.
- Integration with LLM: PromptQL integrates with large language models to elevate the intelligence of data processing.
- User customization: Users can add LLM API keys to tailor their data access solutions according to their needs.
- User-friendly: Provides clear step-by-step guidance on how to connect data, add API keys, and start using AI.
- Performance comparison: Uses ADAB (Agent Data Access Benchmark) to evaluate the performance of existing AI agents/assistants and compare them with PromptQL.
- Open collaboration: Encourages users to collaborate with developers to build and optimize their AI agents.
How to Use
1. Connect your data: Follow PromptQL's instructions to connect your private data sources to the platform.
2. Add your LLM API key: Enter the API key for your chosen large language model in PromptQL for data classification and processing.
3. Build with AI: Use the connected data and LLM to start constructing your agent query plans for automated data access and processing.
4. Assess performance: Evaluate your AI agent/assistant's performance using the Agent Data Access Benchmark (ADAB) and compare it with PromptQL.
5. Optimize the agent: Based on performance assessment results, adjust and optimize your agent query plans to enhance data retrieval efficiency and accuracy.
6. Collaborative development: Work with the PromptQL team to further develop and refine your AI agent to meet specific business needs.
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