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Modern enterprises are rich in data that spans multiple modalities—from text documents and PDFs to presentation slides, images, audio recordings, and more. Imagine asking an AI assistant about your company’s quarterly earnings call: the assistant should not only read the transcript but also “see” the charts in the presentation slides and “hear” the CEO’s remarks. […]
Developing robust text-to-SQL capabilities is a critical challenge in the field of natural language processing (NLP) and database management. The complexity of NLP and database management increases in this field, particularly while dealing with complex queries and database structures. In this post, we introduce a straightforward but powerful solution with accompanying code to text-to-SQL using
Building a custom text-to-SQL agent using Amazon Bedrock and Converse API Read More »
Organizations manage extensive structured data in databases and data warehouses. Large language models (LLMs) have transformed natural language processing (NLP), yet converting conversational queries into structured data analysis remains complex. Data analysts must translate business questions into SQL queries, creating workflow bottlenecks. Amazon Bedrock Knowledge Bases enables direct natural language interactions with structured data sources.
Build conversational interfaces for structured data using Amazon Bedrock Knowledge Bases Read More »
Adobe Inc. excels in providing a comprehensive suite of creative tools that empower artists, designers, and developers across various digital disciplines. Their product landscape is the backbone of countless creative projects worldwide, ranging from web design and photo editing to vector graphics and video production. Adobe’s internal developers use a vast array of wiki pages,
Adobe enhances developer productivity using Amazon Bedrock Knowledge Bases Read More »
For an AI model to perform effectively in specialized domains, it requires access to relevant background knowledge. A customer support chat assistant, for instance, needs detailed information about the business it serves, and a legal analysis tool must draw upon a comprehensive database of past cases. To equip large language models (LLMs) with this knowledge,
Contextual retrieval in Anthropic using Amazon Bedrock Knowledge Bases Read More »
In these days, it is more common to companies adopting AI-first strategy to stay competitive and more efficient. As generative AI adoption grows, the technology’s ability to solve problems is also improving (an example is the use case to generate comprehensive market report). One way to simplify the growing complexity of problems to be solved
Build GraphRAG applications using Amazon Bedrock Knowledge Bases Read More »
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 simple question answering by performing multi-step
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 complex processing pipelines and significant development
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, and a hybrid approach combining
This blog post is co-written with Jonas Neuman from HERE Technologies. HERE Technologies, a 40-year pioneer in mapping and location technology, collaborated with the AWS Generative AI Innovation Center (GenAIIC) to enhance developer productivity with a generative AI-powered coding assistant. This innovative tool is designed to enhance the onboarding experience for HERE’s self-service Maps API
