chore(doc): fix link in readme

Author: krokoko

README.md

@@ -22,7 +22,7 @@ This repo provides samples to demonstrate how to build your own Generative AI so
 |[Contract Compliance Analysis](samples/contract-compliance-analysis/)| This project automates the analysis of contracts by splitting them into clauses, determining clause types, evaluating compliance against a customer's legal guidelines, and assessing overall contract risk based on the number of compliant clauses. This is achieved through a workflow that leverages Large Language Models via Amazon Bedrock. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
 |[Text To SQL](samples/text-to-sql/)| The "Text To SQL" generative AI sample application solution enables users to interact with databases through natural language queries, eliminating the need for extensive SQL knowledge. This application leverages the powerful Anthropic Claude 3 model, hosted on Amazon Bedrock, to translate natural language queries into executable SQL statements seamlessly. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
 |[LlamaIndex Basic DataLoader](samples/llamaindex-basic-data-loader/)| The "LlamaIndex Basic Data Loader" generative AI sample application solution demonstrates the `LlamaIndexDataLoader` from the [Generative AI CDK Constructs](https://github.com/awslabs/generative-ai-cdk-constructs) package. The default implementation uses the [S3 File or Directory Loader](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/readers/llama-index-readers-s3), and can be extended for other [LlamaHub Readers](https://llamahub.ai/?tab=readers). The solution expects [LlamaIndex Documents](https://docs.llamaindex.ai/en/stable/module_guides/loading/documents_and_nodes/) into an output S3 ready for downstream consumer generative AI solutions. | Backend | Python |
-|[RFP Answers with GenAI](samples/rfp-answers-with-genai/)| This project provides guidance on how you can use Knowledge Bases for Amazon Bedrock with custom transformations to create draft answers for Request for Proposal (RFP) documents, streamlining the answer of new potential contracts. This is achieved through a workflow that leverages Large Language Models via Amazon Bedrock. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
+|[RFP Answers with GenAI](samples/rfp-answer-generation/)| This project provides guidance on how you can use Knowledge Bases for Amazon Bedrock with custom transformations to create draft answers for Request for Proposal (RFP) documents, streamlining the answer of new potential contracts. This is achieved through a workflow that leverages Large Language Models via Amazon Bedrock. | Backend + Frontend | Python for Backend, TypeScript (React) for Frontend |
 
 ## Contributing