My main use case for Amazon Q is that we have access to it in our company, and on a daily basis, we receive a lot of requirements from clients to build websites and probably do all the other work as well. I use Amazon Q for debugging, enhancements, and building applications. I also use Amazon Q for summarizing content and summarizing meeting descriptions. On a daily basis, Amazon Q helps me in many ways, which are mostly in developing, and some of my peer group use it for testing purposes as well. We build cool automations using Amazon Q. The project that we are currently doing is totally on AWS. Amazon Q has been the primary source for any questions that I have while implementing the product and the application in AWS. It helps a lot on a daily basis. A recent task where Amazon Q made a difference was regarding a DocumentDB connection and Secret Manager connection issue that I faced today. I was not able to figure out what was really failing in the Lambda, but when I provided the code to Amazon Q and shared the details with it, it was able to determine that we were missing important string details in the Secret Manager that are related to DocumentDB. That was the reason the Lambda was failing to fetch the details from DocumentDB using the Secret Manager in which we store all the details of DocumentDB. It was not an easy task for me because it was a minute detail that I was not able to figure out because everything seemed to be very similar. Amazon Q helped in figuring out the issue. Before this, the actual challenging task of the project, which is setting up all the context of the project and building services and the Lambda layers and then all the Terraform code, has been done in Amazon Q. The project that we are currently doing, most of the work, whether it is Terraform code or Lambda code, which is written in Python, is completely done by Amazon Q. Amazon Q has also taken care of the gateway connections and all the other work. On a daily basis, I use Amazon Q for building the project that we are currently building on AWS. I use it for debugging, development, and all the automations that we have in the project. Apart from that, it helps in participating in hackathons confidently because we have Amazon Q as a backup. The moment we get some idea in the hackathon, we go ahead and build it by prompting the idea to Amazon Q, and it immediately sets up the complete project. The main use case is that it helps in a lot of development work, automation, and debugging as well. The primary areas of focus are development, automation, and debugging. Regardless of the environment that I am in, whether it is in a product development space, an automation space, or a hackathon, I rely on Amazon Q on a daily basis.
I have completed a project where the company required testing R&D on Kubernetes. I tested it locally by installing MiniKube, Kubernetes, and all the containers. I configured the Kubernetes MCB server to Amazon Q. Once the configuration was done, I could manage all the Kubernetes clusters using Amazon Q prompt. When I want to check any pods in the Kubernetes cluster, I do not need to use commands. I can simply write a prompt such as 'Please show me the Kubernetes pods and namespace and all.' Amazon Q automatically provides all the required details.
My main use case for Amazon Q is primarily focused on business perspectives. I was able to do a Q&A sort of interaction. If I simply asked what the revenue was for this quarter, Amazon Q was able to respond with all the references we have within our system, from SharePoint or even from S3. It was very useful because all these works had to be done manually. Now, this is something it was able to search itself, and we are able to save some time using it. Although it was not in my main project at my company, using Amazon Q was more of a personal project that I created using QuickSight. Using Amazon Q, I was able to ask questions and it was able to answer me. When I asked to summarize sales performance for this month, it was able to give me an answer. It was integrated with QuickSight as well, which was something really amazing.
I'm using Amazon Q developer section and generative AI, and mostly I'm using AWS as my primary day-to-day tool. I just got certified two to three months prior, and I've been using Amazon Q since then. I'm pursuing my masters and currently working on different projects related to machine learning, AI, and model developing, so I need a lot of help in coding sometimes. Amazon Q really helps in those types of situations, and when I get stuck, when I don't know what the code is supposed to mean, or if I'm just stuck in a loop and can't go forward, Amazon Q develops and gives me a better solution of all the possibilities and scenarios I could run.
Innovation Strategist at a insurance company with 5,001-10,000 employees
Real User
Top 20
Aug 7, 2025
We have been using AWS services for almost three years, with Amazon Q specifically for the last one and a half years. We use Amazon Q for document-related use cases where we need to summarize documents related to GenAI and find differentiation between financial and legal documents. We are currently exploring integration of Amazon Q into our data analysis and analytics reports. Another use case involves creating a chatbot at the enterprise level where we can integrate all the company's knowledge base to serve as a chatbot for internal employees. Regarding data querying, I am not certain because we have not implemented any particular use case with Amazon Q for this purpose. We have integrated it with our current knowledge base, where it functions effectively. Amazon Q has the potential to work with Amazon Quicksight or Athena, though we have not utilized these features yet.
My team and I were the end users of Amazon Q, and we used it for all our tasks including coding, testing, upgrading, troubleshooting, and security scanning. We primarily used it for code generation use cases where we were converting legacy applications into modern stack applications, though it was not generating the complete solution.
Senior Software Engineer at a tech vendor with 1,001-5,000 employees
Real User
Top 5
Aug 5, 2025
My use cases for Amazon Q involve producing the premium service of Amazon Q that we got access to from the company. We usually use it for different purposes. One of the purposes is that we use it for testing when developing modules and software modules. We run the test cases and write them using it, doing automation testing with it. Apart from this, I often use it to get an idea about the product. For example, I have a code base right now, and I did a plug-in inside my editor, Versus Code, the Amazon Q editor plug-in. I give permission to scan through my code base and get the context of the product to understand the different coding patterns and the code structure. If I plan to work on a new module or feature, I just prompt it with the details, and it gives me the context about the flow of that module, understanding the existing flow of the code base. Additionally, I am writing unit tests with Amazon Q right now.
Site reliability engineer at a tech services company with 201-500 employees
Real User
Top 20
Jul 16, 2025
I use it for personal tasks and work. For personal projects, when I'm working on a weekend hobby, I can use it to create APIs, which helps to bootstrap. If I have a data set which I want to analyze, I give it some prompts, and it's able to analyze. Regarding work, I'm able to use it to correct my code. In regards to security compliance, I ensure I follow the best practices. Most of the time I use it for technical tasks in regards to programming. It's able to actually understand human language regarding the context. For instance, I have an existing folder. I can give it a prompt for it to scan through and generate whether it's following the best practices, or if the code is not following the formatting and linting requirements. Additionally, if there's data I want to analyze, it's able to understand the natural language from the human perspective. I use Amazon for cloud service operations, which is why I opt for Amazon Q. Amazon Q is a valuable tool to boost productivity and helps deliver quality and value to customers.
Amazon Q provides context-aware responses and integrates seamlessly with AWS, supporting efficient cloud task management, multi-language frameworks, and documentation capabilities. It's an asset for diverse development needs with auto-logging, intuitive interfaces, and fast deployment.Amazon Q offers advanced natural language interpretation, enriching productivity with robust features like Git-related insights for tracking code changes and built-in redundancy. It supports multi-language...
My main use case for Amazon Q is that we have access to it in our company, and on a daily basis, we receive a lot of requirements from clients to build websites and probably do all the other work as well. I use Amazon Q for debugging, enhancements, and building applications. I also use Amazon Q for summarizing content and summarizing meeting descriptions. On a daily basis, Amazon Q helps me in many ways, which are mostly in developing, and some of my peer group use it for testing purposes as well. We build cool automations using Amazon Q. The project that we are currently doing is totally on AWS. Amazon Q has been the primary source for any questions that I have while implementing the product and the application in AWS. It helps a lot on a daily basis. A recent task where Amazon Q made a difference was regarding a DocumentDB connection and Secret Manager connection issue that I faced today. I was not able to figure out what was really failing in the Lambda, but when I provided the code to Amazon Q and shared the details with it, it was able to determine that we were missing important string details in the Secret Manager that are related to DocumentDB. That was the reason the Lambda was failing to fetch the details from DocumentDB using the Secret Manager in which we store all the details of DocumentDB. It was not an easy task for me because it was a minute detail that I was not able to figure out because everything seemed to be very similar. Amazon Q helped in figuring out the issue. Before this, the actual challenging task of the project, which is setting up all the context of the project and building services and the Lambda layers and then all the Terraform code, has been done in Amazon Q. The project that we are currently doing, most of the work, whether it is Terraform code or Lambda code, which is written in Python, is completely done by Amazon Q. Amazon Q has also taken care of the gateway connections and all the other work. On a daily basis, I use Amazon Q for building the project that we are currently building on AWS. I use it for debugging, development, and all the automations that we have in the project. Apart from that, it helps in participating in hackathons confidently because we have Amazon Q as a backup. The moment we get some idea in the hackathon, we go ahead and build it by prompting the idea to Amazon Q, and it immediately sets up the complete project. The main use case is that it helps in a lot of development work, automation, and debugging as well. The primary areas of focus are development, automation, and debugging. Regardless of the environment that I am in, whether it is in a product development space, an automation space, or a hackathon, I rely on Amazon Q on a daily basis.
I have completed a project where the company required testing R&D on Kubernetes. I tested it locally by installing MiniKube, Kubernetes, and all the containers. I configured the Kubernetes MCB server to Amazon Q. Once the configuration was done, I could manage all the Kubernetes clusters using Amazon Q prompt. When I want to check any pods in the Kubernetes cluster, I do not need to use commands. I can simply write a prompt such as 'Please show me the Kubernetes pods and namespace and all.' Amazon Q automatically provides all the required details.
My main use case for Amazon Q is primarily focused on business perspectives. I was able to do a Q&A sort of interaction. If I simply asked what the revenue was for this quarter, Amazon Q was able to respond with all the references we have within our system, from SharePoint or even from S3. It was very useful because all these works had to be done manually. Now, this is something it was able to search itself, and we are able to save some time using it. Although it was not in my main project at my company, using Amazon Q was more of a personal project that I created using QuickSight. Using Amazon Q, I was able to ask questions and it was able to answer me. When I asked to summarize sales performance for this month, it was able to give me an answer. It was integrated with QuickSight as well, which was something really amazing.
I'm using Amazon Q developer section and generative AI, and mostly I'm using AWS as my primary day-to-day tool. I just got certified two to three months prior, and I've been using Amazon Q since then. I'm pursuing my masters and currently working on different projects related to machine learning, AI, and model developing, so I need a lot of help in coding sometimes. Amazon Q really helps in those types of situations, and when I get stuck, when I don't know what the code is supposed to mean, or if I'm just stuck in a loop and can't go forward, Amazon Q develops and gives me a better solution of all the possibilities and scenarios I could run.
We have been using AWS services for almost three years, with Amazon Q specifically for the last one and a half years. We use Amazon Q for document-related use cases where we need to summarize documents related to GenAI and find differentiation between financial and legal documents. We are currently exploring integration of Amazon Q into our data analysis and analytics reports. Another use case involves creating a chatbot at the enterprise level where we can integrate all the company's knowledge base to serve as a chatbot for internal employees. Regarding data querying, I am not certain because we have not implemented any particular use case with Amazon Q for this purpose. We have integrated it with our current knowledge base, where it functions effectively. Amazon Q has the potential to work with Amazon Quicksight or Athena, though we have not utilized these features yet.
My team and I were the end users of Amazon Q, and we used it for all our tasks including coding, testing, upgrading, troubleshooting, and security scanning. We primarily used it for code generation use cases where we were converting legacy applications into modern stack applications, though it was not generating the complete solution.
My use cases for Amazon Q involve producing the premium service of Amazon Q that we got access to from the company. We usually use it for different purposes. One of the purposes is that we use it for testing when developing modules and software modules. We run the test cases and write them using it, doing automation testing with it. Apart from this, I often use it to get an idea about the product. For example, I have a code base right now, and I did a plug-in inside my editor, Versus Code, the Amazon Q editor plug-in. I give permission to scan through my code base and get the context of the product to understand the different coding patterns and the code structure. If I plan to work on a new module or feature, I just prompt it with the details, and it gives me the context about the flow of that module, understanding the existing flow of the code base. Additionally, I am writing unit tests with Amazon Q right now.
I use it for personal tasks and work. For personal projects, when I'm working on a weekend hobby, I can use it to create APIs, which helps to bootstrap. If I have a data set which I want to analyze, I give it some prompts, and it's able to analyze. Regarding work, I'm able to use it to correct my code. In regards to security compliance, I ensure I follow the best practices. Most of the time I use it for technical tasks in regards to programming. It's able to actually understand human language regarding the context. For instance, I have an existing folder. I can give it a prompt for it to scan through and generate whether it's following the best practices, or if the code is not following the formatting and linting requirements. Additionally, if there's data I want to analyze, it's able to understand the natural language from the human perspective. I use Amazon for cloud service operations, which is why I opt for Amazon Q. Amazon Q is a valuable tool to boost productivity and helps deliver quality and value to customers.