Generative AI on AWS - Amazon Bedrock, RAG & AWS KIRO [2026]

Explore the fundamentals of generative AI, including how foundation models generate text, images, and other data, with examples from OpenAI's GPT and AWS Nova models on Bedrock.

Foundation models are deep neural networks trained on massive unlabeled internet data, enabling text generation, summarization, Q&A, and code generation, with tokens, parameters, and temperature shaping outputs.

Discover Amazon Bedrock, managed serverless service exposing base models via API from Amazon and third parties; pick a model ID, set prompts, and call Bedrock via console, cli, or sdk.

Explore Amazon Bedrock architecture, including the service and escrow accounts, runtime inference, and model endpoint routing. See how private link and VPC interface endpoints secure requests and data.

Explore the Amazon Bedrock console walkthrough to learn the model catalog, serverless and marketplace models, API keys, testing, and inference options including cross-region, batch, and provisioned throughput.

Explore Amazon Bedrock's multi-agent framework, flows, knowledge bases, and data automation to build generative ai workflows with drag-and-drop simplicity, harness rag and automated reasoning, guardrails, and prompt management.

Explore how inference parameters shape foundation model behavior on Amazon Bedrock, detailing randomness and diversity controls—temperature, top K, top P—and length controls such as max tokens and stop sequences.

Build a production-ready generative AI powered equipment SME assistant to reduce wind turbine downtime by summarizing field incident logs with Amazon Bedrock foundation models, boosting productivity and revenue.

Design a PoC-to-production workflow that streams wind turbine logs from a streamlet UI through API gateway to Lambda, using Amazon Bedrock Nova Pro with system prompts and inference parameters.

Learn how to invoke Amazon Bedrock from Lambda by crafting the request with the model id, a body containing user input and inference parameters, and optional guardrails and latency settings.

Set up observability for generative AI on AWS by enabling Bedrock logging and CloudWatch metrics. Configure log groups, retention, data types (text, images, embeddings), and alarms with SNS.

Apply Amazon Bedrock Guardrails to protect generative AI apps with content filters, denied topics, word filters, sensitive information filters, and contextual grounding checks to mitigate hallucinations and toxicity.

Enable bedrock guardrails in the equipment SME assistant by configuring guardrail id and version, deploying code, and validating through test events and api gateway to ensure toxic prompts are blocked.

Learn how to select foundation models for your use case by evaluating modality, context window, guardrails, cost, latency, and regional availability, using Bedrock and Nova Pro as examples.

Learn Bedrock model evaluation strategies for a GenAI equipment SME app, using programmatic, llm-as-judge, and human evaluations with prompt datasets, jsonl prompts, reference responses, and metrics.

Optimize foundation model outputs through prompt engineering by crafting contextual prompts, clear task and output specifications, and exploring zero-shot to few-shot prompting plus retrieval augmented generation.

Explore hands-on model distillation on AWS Bedrock by creating a distillation job that trains a student model from a larger teacher model using synthetic data and S3 datasets.

Utilize supervised fine tuning with labeled prompt–response data on Amazon Bedrock, triggering a SageMaker training job to tailor a model's style and format for tasks like summarization and Q&A.

Explore a mental model for foundation model adaptation strategies, including prompt engineering, retrieval augmented generation, distillation, fine-tuning, and continued pre-training. Evaluate trade-offs in speed, cost, accuracy, and enterprise data use.

Builds a chat backend by wiring a conversation memory buffer, an Amazon Bedrock LM, and a conversation chain, then invokes the model to generate replies.

Build a chatbot frontend with Streamlit, importing the backend and memory, customizing the UI, and running the app to connect OpenAI or Amazon Bedrock.

Enable Bedrock model invocation logging to CloudWatch, configure log groups and storage options (CloudWatch, S3, or both), and capture request/response metadata, tokens, and latency for all model invocations.

Architect a HR Q&A app using retrieval, augmented, and generation, built with a data ingestion workflow that loads pdfs, creates vector embeddings, and stores them in FAISS for Bedrock-powered responses.

Set up your chat bot environment in vscode via Anaconda navigator, then install flask, SQL alchemy, py pdf, and the Facebook AI Similarity search vector store to start coding.

Build the rack frontend with Streamlit and connect it to the rack backend to create an end-to-end hr q&a app with rag.

Understand how the Amazon Bedrock Knowledge Base powers retrieval augmented generation by automating data ingestion: load from S3, chunk, embed with Cohere or Titan, and store in a vector store.

Build an AWS API Gateway REST API that triggers a Lambda function to query the Bedrock knowledge base with a user prompt and return a generated answer.