Tag: vector databases
Retrieval-Augmented Generation (RAG): How to Ground AI in Verified Sources
Learn how Retrieval-Augmented Generation (RAG) reduces AI hallucinations by grounding LLM outputs in verified sources. Explore the architecture, benefits over fine-tuning, and best practices for enterprise deployment.
- Jun 7, 2026
- Collin Pace
- 0
- Permalink
RAG with Vector Databases: Embeddings, HNSW Indexing, and Filters
Learn how Retrieval-Augmented Generation (RAG) uses vector databases, embeddings, and HNSW indexing to reduce AI hallucinations and improve accuracy with real-time data.
- May 6, 2026
- Collin Pace
- 0
- Permalink
Representation Learning in Generative AI: How Embeddings Capture Meaning
Embeddings in generative AI turn words, images, and sounds into numerical vectors that capture meaning. They power search, generation, and detection-making AI understand context, not just keywords.
- Mar 11, 2026
- Collin Pace
- 6
- Permalink