Insights That Move Technology Forward
2 min read
Fine Tuning
As LLMs take center stage in many AI initiatives, one critical decision teams must make is whether to fine-tune the model or build a retrieval-augmented system that fetches external knowledge on demand. The answer depends heavily on the use case, data dynamics, and long-term needs.
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9 min read
Comparing Modern Vector Databases for Retrieval-Augmented Generation: A Detailed Analysis of Cost and Usability
In the rapidly evolving landscape of artificial intelligence, Retrieval-Augmented Generation (RAG) has emerged as a powerful technique to enhance the capabilities of large language models (LLMs). RAG combines retrieval-based methods, which fetch relevant information from a knowledge base, with generative models to produce contextually accurate and informative responses. At the heart of RAG systems lies the vector database, a specialized database designed to store, index, and query high-dimensional vector embeddings—numerical representations of data such as text, images, or audio. These embeddings capture semantic meaning, enabling efficient similarity searches critical for RAG applications like question answering, summarization, and dialog systems.
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