In this blog, we’ll explore the core differences between AI in applications and AI RAG, how they work, and where they are most effectively applied.
What is AI in Applications?
AI in applications refers to the integration of artificial intelligence models into software systems to automate tasks, analyse data, make predictions, and enhance decision-making. These AI models are typically trained on static datasets and operate based on patterns learned from historical data.
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What is AI with RAG (Retrieval-Augmented Generation)?
Retrieval-Augmented Generation (RAG) is a more dynamic form of AI that combines two key components:
1. Retrieval – The AI model searches external knowledge bases or documents to gather relevant information in real-time.
2. Generation – The AI model (typically a large language model, or LLM) synthesises a response using both the retrieved information and its internal training data.
RAG enhances traditional AI capabilities by allowing the model to pull in updated or external information at the time of query, enabling more accurate and context-aware responses.