Knowledge graphs offer a significant improvement over traditional Retrieval-Augmented Generation (RAG) systems by understanding and preserving the relationships and context between entities, thus preventing the inaccuracies and misinterpretations typical of RAG. In a workshop using Cognee and dlt, participants learn to create knowledge graphs from API documentation, enabling precise data retrieval and eliminating AI-generated guesses. The workshop demonstrates how these tools transform structured data into a queryable knowledge graph, enhancing the ability to answer specific technical questions and make connections across different documentation sources. By employing ontologies, users can define terms like "endpoint" clearly, which helps build a precise and reliable documentation system, showcased through practical exercises such as transforming the NYC taxi dataset and creating a queryable graph from API documentation. The workshop, available in a 90-minute session with accompanying Colab notebooks, provides insights into production deployment strategies and practical demonstrations, offering a comprehensive guide to building effective knowledge graphs.