Energy Efficiency AI
In collaboration with Cake Studio, we're upgrading an energy efficiency consulting firm's information retrieval system to an API-based web application. This application analyzes technical documents from energy efficiency manuals, enabling energy efficiency professionals to answer complex questions quickly and accurately.
Using top-tier AI models and retrieval techniques, we're able to accurately answer complex questions, even when the answer includes complex calculations or tables. We also provide the client with the option to customize the AI models and retrieval techniques to better fit their specific needs. This optimizes system performance, response quality, and cost.
Our Approach
Our team is creating:
- A sophisticated prompting strategy
- Retrieval-Augmented Generation (RAG) implementation
- A user-friendly web interface
The application uses API calls and RAG to answer complex questions, outperforming the previous GPT-based solution.
We're comparing techniques like:
- Context caching (for small, custom applications)
- Semantic search
- Cosine similarity
- Contextual embeddings
- Knowledge graphs
Impact
Our approach improves how energy professionals use technical information. It gives users quick access to accurate information, enhancing their productivity and accuracy. This tool helps deliver effective energy efficiency solutions across various projects, commercial or residential.
Technologies Used
- Retrieval-augmented generation (RAG)
- Semantic search
- Cosine similarity
- Knowledge graphs
- API-based web application development
Key Benefits
- Rapid access to accurate technical information
- Enhanced productivity for energy efficiency professionals
- Improved accuracy in energy efficiency solutions
- Customizable AI models and retrieval techniques
- Cost-effective information retrieval system