Background
This project’s client is a leading B2B and B2C data information platform provider. This provider offers comprehensive statistics and complex marketing reports, making navigation challenging. They sought a centralized search tool to deliver accurate, natural language answers from their vast database, enhancing user experience and efficiency.
Challenge
The primary challenges were:
- Natural Language Search Tool: Creating a tool that could process natural language queries and provide coherent answers with source links.
- Balancing Quality and Costs: Ensuring the solution maintained high quality while keeping costs low.
Solution
Dockside Data addressed these challenges with a strategic approach:
- Natural Language Processing: Developed an advanced search tool using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to understand queries and generate natural language responses, complete with source links.
- Testing Framework: Established a robust testing framework to measure and ensure the tool’s speed, cost-efficiency, and quality.
Optimization of Algorithms: Improved the database and search algorithms. As well as conducting a series of experiments trying various RAG approaches to enhance performance and accuracy.
Impact
The provider now offers a seamless, natural language search experience, significantly improving usability and customer support. The optimized algorithms and testing framework ensured a solution that is measurable fast, cost-effective, and of high quality.
Conclusion
Dockside Data’s approach in natural language processing and optimization effectively addressed the provider’s challenges, enhancing their customers’ ability to retrieve data and improving the overall user experience.
