RAG combines retrieval and generation to enhance AI responses by integrating external knowledge sources.
Read moreVector Database – Definition and Use Cases in AI
Vector databases store and search embeddings efficiently, crucial for semantic search and AI retrieval systems.
Read moreWhat Does LLM Stand For? – Full Form and Meaning
LLM stands for Large Language Model, advanced AI systems trained on massive datasets to understand and generate text.
Read moreWhat is Embedding in AI? – Definition and Meaning
Embedding in AI represents data like text or images as numerical vectors, enabling models to understand relationships.
Read moreHow Fine-tuning Works – Explained with Examples
Fine-tuning adjusts pre-trained AI models to specific tasks, improving accuracy without full retraining.
Read moreTokenization in AI – Definition and Use Cases
Learn how tokenization in AI helps models process text by breaking it into tokens for better language understanding.
Read moreWhat is Tokenization in AI? – Definition, Meaning & Examples
🧩 Definition Tokenization in Artificial Intelligence (AI) refers to the process of breaking text into smaller, meaningful units called tokens — which can be words, subwords, or even characters.These tokens act as the basic input elements that AI models, especially
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