RAG

In: Artificial Intelligence

Generative AI has revolutionized customer interactions across industries by offering personalized, intuitive experiences powered by unprecedented access to information. This transformation is further enhanced by Retrieval Augmented Generation (RAG), a technique that allows large language models (LLMs) to reference external knowledge sources beyond their training data. RAG has gained popularityContinue Reading

In: Artificial Intelligence

In the pharmaceutical industry, biotechnology and healthcare companies face an unprecedented challenge for efficiently managing and analyzing vast amounts of drug-related data from diverse sources. Traditional data analysis methods prove inadequate for processing complex medical documentation that includes a mix of text, images, graphs, and tables. Amazon Bedrock offers featuresContinue Reading

In: Artificial Intelligence

Agentic Retrieval Augmented Generation (RAG) applications represent an advanced approach in AI that integrates foundation models (FMs) with external knowledge retrieval and autonomous agent capabilities. These systems dynamically access and process information, break down complex tasks, use external tools, apply reasoning, and adapt to various contexts. They go beyond simpleContinue Reading

In: Artificial Intelligence

Organizations today deal with vast amounts of unstructured data in various formats including documents, images, audio files, and video files. Often these documents are quite large, creating significant challenges such as slower processing times and increased storage costs. Extracting meaningful insights from these diverse formats in the past required complexContinue Reading

In: Artificial Intelligence

Foundation models (FMs) have revolutionised AI capabilities, but adopting them for specific business needs can be challenging. Organizations often struggle with balancing model performance, cost-efficiency, and the need for domain-specific knowledge. This blog post explores three powerful techniques for tailoring FMs to your unique requirements: Retrieval Augmented Generation (RAG), fine-tuning,Continue Reading

In: Artificial Intelligence

Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. This data is used to enrich the generative AI prompt to deliver more context-specific and accurate responses without continuously retrainingContinue Reading

In: Artificial Intelligence

Retrieval Augmented Generation (RAG) applications have become increasingly popular due to their ability to enhance generative AI tasks with contextually relevant information. Implementing RAG-based applications requires careful attention to security, particularly when handling sensitive data. The protection of personally identifiable information (PII), protected health information (PHI), and confidential business dataContinue Reading

In: Artificial Intelligence

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies and AWS. Amazon Bedrock Knowledge Bases offers fully managed, end-to-end Retrieval Augmented Generation (RAG) workflows to create highly accurate, low-latency, secure, and custom generative AI applications by incorporating contextual informationContinue Reading

In: Artificial Intelligence

Organizations deploying generative AI applications need robust ways to evaluate their performance and reliability. When we launched LLM-as-a-judge (LLMaJ) and Retrieval Augmented Generation (RAG) evaluation capabilities in public preview at AWS re:Invent 2024, customers used them to assess their foundation models (FMs) and generative AI applications, but asked for moreContinue Reading