Browse all content tagged with RAG
Cache Augmented Generation (CAG) is a novel approach to enhancing large language models (LLMs) by preloading knowledge as precomputed key-value caches, enabling low-latency, accurate, and efficient AI performance for static knowledge tasks.
Document grading in Retrieval-Augmented Generation (RAG) is the process of evaluating and ranking documents based on their relevance and quality in response to a query, ensuring that only the most pertinent and high-quality documents are used to generate accurate, context-aware responses.
Document reranking is the process of reordering retrieved documents based on their relevance to a user's query, refining search results to prioritize the most pertinent information. It is a key step in Retrieval-Augmented Generation (RAG) systems, often combined with query expansion to enhance both recall and precision in AI-powered search and chatbots.
FlowHunt's Document Retriever enhances AI accuracy by connecting generative models to your own up-to-date documents and URLs, ensuring reliable and relevant answers using Retrieval-Augmented Generation (RAG).
FlowHunt's GoogleSearch component enhances chatbot accuracy using Retrieval-Augmented Generation (RAG) to access up-to-date knowledge from Google. Control results with options like language, country, and query prefixes for precise and relevant outputs.
Knowledge Sources make teaching the AI according to your needs a breeze. Discover all the ways of linking knowledge with FlowHunt. Easily connect websites, documents, and videos to enhance your AI chatbot's performance.
LazyGraphRAG is an innovative approach to Retrieval-Augmented Generation (RAG), optimizing efficiency and reducing costs in AI-driven data retrieval by combining graph theory and NLP for dynamic, high-quality query results.
Boost AI accuracy with RIG! Learn how to create chatbots that fact-check responses using both custom and general data sources for reliable, source-backed answers.
Query Expansion is the process of enhancing a user’s original query by adding terms or context, improving document retrieval for more accurate and contextually relevant responses, especially in RAG (Retrieval-Augmented Generation) systems.
Question Answering with Retrieval-Augmented Generation (RAG) combines information retrieval and natural language generation to enhance large language models (LLMs) by supplementing responses with relevant, up-to-date data from external sources. This hybrid approach improves accuracy, relevance, and adaptability in dynamic fields.
Explore how OpenAI O1's advanced reasoning capabilities and reinforcement learning outperform GPT4o in RAG accuracy, with benchmarks and cost analysis.
Retrieval Augmented Generation (RAG) is an advanced AI framework that combines traditional information retrieval systems with generative large language models (LLMs), enabling AI to generate text that is more accurate, current, and contextually relevant by integrating external knowledge.
Discover what a retrieval pipeline is for chatbots, its components, use cases, and how Retrieval-Augmented Generation (RAG) and external data sources enable accurate, context-aware, and real-time responses.
Discover the key differences between Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) in AI. Learn how RAG dynamically retrieves real-time information for adaptable, accurate responses, while CAG uses pre-cached data for fast, consistent outputs. Find out which approach suits your project's needs and explore practical use cases, strengths, and limitations.
