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Introduction: Understanding Embedding Models and Semantic Spaces Imagine a giant document warehouse where every word, phrase, and sentence has a physical location. In this warehouse, phrases with similar meanings or that are commonly used together are stored close to one another, while words with no relation are placed far apart. This is how embedding models
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Ambiguity in user prompts is one of the more significant challenges for Retrieval-Augmented Generation (RAG) systems striving to deliver precise and actionable responses. For any system with impactful consequences, such as medical diagnostics, financial actions, asset management (e.g. cloud computing), and similar domains, it is crucial to develop strategies that can identify, interpret, and resolve
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Many Organizations are quite eager to adopt the latest AI advancements, skipping traditional machine learning (ML) solutions and diving straight into generative AI (GenAI), especially with large language models (LLMs). The attraction to LLMs is clear: their ability to handle data analysis, generate content for marketing and documentation, and even tackle customer-facing applications like support
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