Ranvijay Kumar
Introduction to Devin In the rapidly evolving landscape of technology, a new breakthrough has eme...
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Appetenza
Most Retrieval Augmented Generation (RAG) systems are built around text documents. However, real...
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Appetenza
Memory is what transforms a simple question-answer bot into a true conversational assistant. In Retrieval Augmented Generation (RAG) systems, memory allows chatbots to remember previous messages, user preferences, and past interactions. Without memory, every question feels like the first conve...
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The Large Language Model (LLM) is the generation engine in a Retrieval Augmented Generation (RAG) system. While retrieval brings relevant information, the LLM is responsible for understanding that context and producing a clear, helpful response. Choosing the right LLM is an important decision ...
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Prompt engineering is one of the most powerful tools in a Retrieval Augmented Generation (RAG) system. Even with perfect retrieval and high-quality embeddings, a poorly designed prompt can cause an LLM to ignore context, invent information, or produce vague answers. In production environments,...
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Retrieval quality is the most important factor in a successful Retrieval Augmented Generation (RAG) system. Even the most powerful language model cannot give a good answer if the retrieved context is weak or irrelevant. Hybrid search and re-ranking are two advanced techniques that significantl...
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Appetenza
Vector databases are the search engines behind modern AI systems. In a Retrieval Augmented Generation (RAG) pipeline, embeddings transform text into vectors, and vector databases store and search those vectors efficiently. Without a vector database, semantic search would be too slow and impracti...
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