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Fixed a typo in rag-solutions > README.md #282

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Fixed a typo in rag-solutions > README.md #282

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2 changes: 1 addition & 1 deletion rag-solutions/contextual-chatbot-using-knowledgebase/README.md
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Expand Up @@ -6,7 +6,7 @@ One way to enable more contextual conversations is by linking the chatbot to int

The popular architecture pattern of [Retrieval Augmented Generation](https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-foundation-models-customize-rag.html) (RAG) is often used to augment user query context and responses. RAG combines the capabilities of LLMs with the grounding in facts and real-world knowledge that comes from retrieving relevant texts and passages from corpus of data. These retrieved texts are then used to inform and ground the output, reducing hallucination and improving relevance.

This sample illustrates contextually enhancing a chatbot by using [Knowledge Bases for Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html), aa fully managed serverless service. The Knowledge Bases for Amazon Bedrock integration allows our chatbot to provide more relevant, personalized responses by linking user queries to related information data points. Internally, [Amazon Bedrock](https://aws.amazon.com/bedrock/) uses embeddings stored in a vector database to augment user query context at runtime and enable a managed RAG architecture solution. We use the [Amazon letters to share holders](https://ir.aboutamazon.com/annual-reports-proxies-and-shareholder-letters/default.aspx) dataset to develop this solution.
This sample illustrates contextually enhancing a chatbot by using [Knowledge Bases for Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html), a fully managed serverless service. The Knowledge Bases for Amazon Bedrock integration allows our chatbot to provide more relevant, personalized responses by linking user queries to related information data points. Internally, [Amazon Bedrock](https://aws.amazon.com/bedrock/) uses embeddings stored in a vector database to augment user query context at runtime and enable a managed RAG architecture solution. We use the [Amazon letters to share holders](https://ir.aboutamazon.com/annual-reports-proxies-and-shareholder-letters/default.aspx) dataset to develop this solution.

## Retrieval Augmented Generation

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