Generative AI models are remarkable. But the technology is just table stakes. Maximizing accuracy is your winning hand.
If you don’t want to gamble your authenticity, credibility or reputation, download this whitepaper and discover how to develop more trustworthy and knowledgeable AI applications with our unique approach to Retrieval-Augmented Generation (RAG).
Whether you're a data architect or an AI developer, this in-depth technical guide gives you a blueprint of how to build a robust semantic RAG architecture—from ingestion to prompt generation—with code samples, practical tips and a real-world R&D example. Learn how to enhance the context, traceability and explainability of LLM results and ready your data for scalable, enterprise-grade GenAI.
What You’ll Learn:
Why knowledge graphs are essential for taming AI hallucinations—and how to design, build and enrich a knowledge graph to ground the LLM in facts.
How to provide the right context and content to the LLM—with code samples for splitting, classifying and vectorizing long-form content.
How to retrieve the most relevant information—and orchestrate a hybrid search pipeline to rerank results using lexical, semantic and similarity queries.
Download the whitepaper now and start building AI applications that understand your business as well as you do.