Category: Generative AI

This post discusses how enterprises can build accurate, transparent, and secure generative AI applications while keeping full control over proprietary data. The proposed solution is a RAG pipeline using an AI-native technology stack, whose components are designed from the ground up with AI at their core, rather than having AI capabilities added as an afterthought. We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace.

Enterprises have access to massive amounts of data, much of which is difficult to discover because the data is unstructured. Conventional approaches to analyzing unstructured data use keyword or synonym matching. They don’t capture the full context of a document, making them less effective in dealing with unstructured data. In contrast, text embeddings use machine […]

With the rapid adoption of generative AI applications, there is a need for these applications to respond in time to reduce the perceived latency with higher throughput. Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond. Large language models (LLMs) are a […]

Generative AI has opened up a lot of potential in the field of AI. We are seeing numerous uses, including text generation, code generation, summarization, translation, chatbots, and more. One such area that is evolving is using natural language processing (NLP) to unlock new opportunities for accessing data through intuitive SQL queries. Instead of dealing […]

This blog is co-written with Josh Reini, Shayak Sen and Anupam Datta from TruEra Amazon SageMaker JumpStart provides a variety of pretrained foundation models such as Llama-2 and Mistal 7B that can be quickly deployed to an endpoint. These foundation models perform well with generative tasks, from crafting text and summaries, answering questions, to producing […]

Generative AI agents are capable of producing human-like responses and engaging in natural language conversations by orchestrating a chain of calls to foundation models (FMs) and other augmenting tools based on user input. Instead of only fulfilling predefined intents through a static decision tree, agents are autonomous within the context of their suite of available […]

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It provides access to the most comprehensive set of tools for each step of ML development, from preparing data to building, training, […]

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance. We then use a large model inference container powered by […]

Large language models (or LLMs) have become a topic of daily conversations. Their quick adoption is evident by the amount of time required to reach a 100 million users, which has gone from “4.5yrs by facebook” to an all-time low of mere “2 months by ChatGPT.” A generative pre-trained transformer (GPT) uses causal autoregressive updates […]

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback. When prompted correctly, these models can carry coherent conversations without any task-specific training data. […]