01 · Case study
AI Research Assistant
RAG System — AI assistant that answers questions about documents using RAG and reliable sources.
Researchers and professionals often spend hours searching through lengthy PDFs, reports, and research papers to find specific information. Relevant answers are scattered across multiple documents, making research slower and harder to verify.
The main constraint is the volume and unstructured nature of the information. Manually reading and cross-referencing documents takes time, while simple keyword searches often miss context and fail to provide reliable, source-backed answers.
- Use keyword-based search to find relevant information.
- Send entire documents directly to the LLM.
- Use semantic search with embeddings and RAG. Semantic search captures context and meaning, making it easier to find relevant information even when the exact keywords are not present.
- Retrieve only the most relevant document chunks before generating an answer. This reduces token usage and improves accuracy by providing the model with focused, source-backed context.
AI Research Assistant centralizes multiple sources in a single environment, reducing the time spent manually searching and comparing information. Using RAG, the tool retrieves the most relevant document passages and generates source-backed, contextualized answers, making research faster, more organized, and reliable.
Screenshots coming soon.