Elite Context Generation
for Autonomous LLMs

The diagram above shows the flow of how your query is processed inside our Retrieval-Augmented Generation (RAG) system. We combine three complementary strategies to ensure the most relevant and reliable results:

• Keyword search for straightforward matches in your data.
• Entity extraction & knowledge graph lookups to understand relationships and properties.
• Semantic search in a vector database with multiple query reformulations to capture meaning, not just words.

All findings are gathered, de-duplicated by the LLM, and returned as a single, precise answer. This multi-path approach gives your model both breadth (wide coverage of possible sources) and depth (rich, contextual information).