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Introduction In part one of the "Understanding RAG" series, we covered the basics and advanced concepts of Retrieval-Augmented Generation (RAG). This part delves deeper into the retrieval component and explores enhancement strategies. In this blog, we will explore techniques to improve retrieval and make pre-retrieval processes, such as

What is a RAG? Retrieval-augmented generation (RAG) is a process designed to enhance the output of a large language model (LLM) by incorporating information from an external, authoritative knowledge base. This approach ensures that the responses generated by the LLM are not solely dependent on the model's training

What is "hallucinations" in LLM? Significant advancements in Natural Language Processing (NLP) have been achieved with the advent of Large Language Models (LLMs). These models excel in generating coherent and contextually relevant text. However, they are prone to "hallucinations." In the context of LLMs, hallucinations refer

What is LongRAG? What's the buzz around this? In this blog post, we are taking references from the LongRAG research paper to provide a detailed explanation of LongRAG. LongRAG (Long Retrieval-Augmented Generation) is a framework that uses long-context language models to enhance traditional Retrieval-Augmented Generation (RAG) methods. In

Since Anthropic released “Claude 3.5 Sonnet,” it has been all the rage on social media. Anthropic has claimed that it outperforms leading industry models, including its previous flagship, Claude 3 Opus model. It’s 2x faster and 5x cheaper than Claude 3 Opus.  It is also available via the

As LLMs advance, evaluating their quality is increasingly complex. Using a single large model like GPT-4 as a judge is costly and biased. A research paper by the Cohere team suggests using a Panel of LLM evaluators (PoLL) with smaller models for more accurate, unbiased, and cost-effective assessments. What’s

Introduction Large Language Models (LLMs) have transformed many natural language processing tasks, showing impressive abilities in general knowledge reasoning. However, adapting these models to specialized areas like legal documents, medical records, or specific company data is still challenging. This paper aims to enhance the performance of LLMs in domain-specific question-answering