RT Avi Chawla (@_avichawla)How to build a RAG app on AWS!The visual below shows the exact flow of how a simple RAG system works inside AWS, using services you already know.At its core, RAG is a two-stage pattern:- Ingestion (prepare knowledge)- Querying (use knowledge)Below is how each stage works in practice.> Ingestion: Turning raw data into searchable knowledge- Your documents live in S3 or any internal data source.- Whenever something new is added, a Lambda ingestion function kicks in.- It cleans, proce ...

Generative AI and Agent Technology - Amazon Web Services (AWS) specializes in generative AI, evolving from machine learning [1] - The presentation focuses on deploying generative AI agents to cloud scale, targeting both developers and leaders [1] - The core components of an agent include a model for natural language understanding, a prompt defining the agent's role, an agentic loop for processing input and using tools, history for maintaining context, and tools for external interaction [1][2] - AWS Bedrock offers a suite of capabilities for building generative AI components, including models from Anthropic, Meta, and Mistral [2] - Amazon Bedrock Agents is a fully managed service for deploying agents without infrastructure management [2] Practical Implementation and Tools - The demonstration uses a simple Python agent with a dice rolling tool, initially running locally on a laptop with the Llama 3 8 billion parameter model [1] - The agent is configured with instructions (similar to a prompt) and action groups, which connect to tools [2] - Lambda functions are used to host the tools, enabling them to perform various actions, including interacting with other AWS services [2] - The AWS console provides a user interface for creating and configuring agents, including defining parameters and descriptions for tools [3][4][5][6][7][8][9][10][11][12][13][14][15] - Amazon Q developer is integrated into the console's code editor, offering code suggestions [17][18][19][20][21] Deployment and Scalability - The presentation emphasizes deploying agents to a production-ready, cloud-scale environment [1] - Infrastructure as code frameworks like Terraform, Palumi, and CloudFormation can be used for deployment [3] - AWS offers free courses on deeplearning.ai with AWS environments for experimenting with Amazon Bedrock Agents [25]

AI Development & Trends - AI's evolution is shifting from narrow, specific applications to general AI models capable of diverse tasks, enhancing both utility and power [22][23] - The industry is developing AI models that mimic human thinking processes, incorporating both intuitive (System 1) and reasoning-based (System 2) approaches [25][26] - O1, an AI model, has achieved an IQ score surpassing the human average, indicating advancements in AI's cognitive abilities [23][24] AI Capabilities & Applications - AI can reconstruct sounds and images from brain activity, revealing potential to mirror aspects of ourselves that are not yet fully understood [15][17] - AI is being developed for everyday applications, including domestic robots (e.g, 1X) for household chores and devices (e.g, Morpheus 1) to induce lucid dreaming [19][21] - Modern AI models, like GPT, predict the next word in a sequence, and by concatenating this process, they can generate human-like language [6][9] AI Limitations & Future Outlook - Current AI models, such as GPT4 with 1800 billion parameters, are still significantly smaller than the human brain, which has approximately 700 trillion parameters [27] - The industry emphasizes the need to accelerate AI development to remain competitive, as leadership in AI will likely determine success in other fields [28][29] - AI models are essentially mirrors of the data they are trained on, reflecting the collective knowledge and biases present in the vast amount of text available on the internet [11][13]