# aws-samples/generative-ai-use-cases

Application implementation with business use cases for safely utilizing generative AI in business operations

Repository: https://github.com/aws-samples/generative-ai-use-cases
Canonical: https://ross.abutalabs.com/products/generative-ai-use-cases
Homepage: https://aws-samples.github.io/generative-ai-use-cases/en/
Language: TypeScript
License: MIT-0
License Family: permissive
Topics: aws, bedrock, generative-ai, chatbot, image-generation, llm, rag, sagemaker, lambda, react, typescript, claude, mistral, claude3, command-r, llama3, deepseek-r1, nova, claude4
Last push: 2026-08-16T23:12:55+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 93, longevity 79
- inputs: {"age_days": 1113, "days_push": 17, "days_rel": 45, "gap_med": 6.0, "n_releases_24m": 29}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1379, forks 433 (observed 2026-08-28T04:04:33.684139+00:00)

## What it is
Generative AI Use Cases (GenU) is a well-architected sample application from AWS that demonstrates business use cases for generative AI, built on Amazon Bedrock with a React/TypeScript frontend and serverless backend. It ships with ready-made use cases such as chat, RAG chat, text generation, summarization, and agent chat, plus builders for custom use cases and agents.

## Use cases
- deploy a secure enterprise chatbot on AWS Bedrock
- build a RAG chatbot over internal company documents
- try out generative AI business use cases before building custom solutions
- create custom LLM use cases from prompt templates without coding
- build and deploy AI agents with MCP servers on AWS
- experiment with prompt engineering against multiple LLMs
- automate internal inquiries with a document-grounded assistant

## When to choose
- you want a production-ready, well-architected reference implementation for generative AI on AWS
- your organization uses Amazon Bedrock and wants secure, deployable AI use cases
- you need RAG, agent, and chat capabilities out of the box with customization options
- you want to evaluate multiple LLMs (Claude, Mistral, Llama, DeepSeek, Nova) in one platform

## When to avoid
- you are not using AWS or want a cloud-provider-agnostic solution
- you need a lightweight library or SDK to embed in an existing app rather than a full application
- you want a simple open-source chat UI without AWS infrastructure dependencies
- you need fine-grained control over the full stack beyond what a sample application provides

## Facets
- artifact type: application
- maturity: active
- function: chatbot, rag, llm-inference, agent-framework, prompt-engineering, chat-interface, image-processing, web-framework
- domain: artificial-intelligence, large-language-models, chatbots, web-development, cloud-computing
- platform: cloud, serverless
- tags: amazon-bedrock, generative-ai, enterprise-ai, use-case-builder, agent-builder, aws-samples, self-hosted-ai, llm-chat, retrieval-augmented-generation, ai-agents, web-server, typescript, react, sagemaker, bedrock

## Member repositories
- aws-samples/generative-ai-use-cases (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:33.684139+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:40:18.261562+00:00, confidence not recorded.
  - readme: https://github.com/aws-samples/generative-ai-use-cases (fetched 2026-08-28T04:04:33.684139+00:00, sha 9ac7902e70a0)
  - homepage: https://aws-samples.github.io/generative-ai-use-cases/en/ (fetched 2026-08-29T11:56:37.047676+00:00, sha 9d7e46110944)
- Data as of 2026-08-30T08:39:29.467469+00:00.