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Crafting new questions for exams and quizzes could be tedious and time-consuming for educators. The time required varies based mostly on elements like material, query sorts, expertise stage, and sophistication stage. A number of-choice questions require substantial time to generate high quality distractors and guarantee a single unambiguous reply, and composing efficient true-false questions calls for cautious effort to keep away from vagueness and assess deeper understanding. Creating high-quality evaluation questions of any format necessitates meticulous consideration to element from educators with a purpose to produce honest and legitimate pupil evaluations. To streamline this cumbersome course of, we suggest an automatic examination era resolution based mostly on Amazon Bedrock.

On this publish, we discover how one can construct an software that generates exams tailor-made to your personal lecture content material. We cowl the technical implementation utilizing the Anthropic Claude massive language mannequin (LLM) on Amazon Bedrock and AWS Lambda deployed with the AWS Serverless Utility Mannequin (AWS SAM). This resolution allows educators to immediately create curriculum-aligned assessments with minimal effort. College students can take customized quizzes and get quick suggestions on their efficiency. This resolution simplifies the examination creation course of whereas benefiting each academics and learners.

Amazon Bedrock

Amazon Bedrock is a totally managed service that gives a selection of high-performing basis fashions (FMs) from main synthetic intelligence (AI) corporations like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon utilizing a single API, together with a broad set of capabilities it’s worthwhile to construct generative AI purposes with safety, privateness, and accountable AI. On this publish, we concentrate on a textual content era use case, and might select from Amazon Titan Textual content G1 and different fashions on Amazon Bedrock, together with Anthropic Claude, AI21 Labs Jurassic, Meta Llama 2, and Cohere Command.

With the power to scale as much as 200,000-token context home windows, Anthropic Claude v2.1 on Amazon Bedrock is our most well-liked selection for this publish. It’s usually useful when working with prolonged paperwork equivalent to complete books. Once we speak about tokens, we discuss with the smallest particular person “atoms” of a language mannequin, and might varyingly correspond to phrases, subwords, characters, and even bytes (within the case of Unicode). For Anthropic Claude on Amazon Bedrock, the common token is about 3.5 English characters. The 200,000 tokens supported by Anthropic Claude v2.1 on Amazon Bedrock can be equal to roughly 150,000 phrases or over 500 pages of paperwork.

This publish demonstrates how one can use superior prompt engineering to regulate an LLM’s conduct and responses. It exhibits how one can randomly generate questions and solutions from lecture information, applied as a easy serverless software.

Answer overview

The next diagram illustrates the appliance structure. We distinguish two paths: the educator path (1) and the learner path (2).

As first-time customers, each educator and learner want to finish the sign-up course of, which is completed by two separate Amazon Cognito consumer swimming pools. For the educator, when the sign-up is full, Amazon Cognito invokes the Lambda operate referred to as CognitoPostSignupFn to subscribe the educator to an Amazon Easy Notification Service (Amazon SNS) matter. The educator should approve the subscription to this matter with a purpose to be notified by e mail with the scorecard of every learner who will likely be taking the generated examination.

Determine 1: Architectural diagram of the examination generator software

The workflow contains the next steps:

  1. The educator opens the touchdown web page for producing an examination underneath the area gen-exam.<your-domain-name> via Amazon Route 53, which redirects the request to the Utility Load Balancer (ALB).

1.1 The ALB communicates with Amazon Cognito to authenticate the educator on the educator consumer pool.

1.2 The educator uploads a lecture as a PDF file into the examination era front-end.

1.3 The Amazon Elastic Container Service (Amazon ECS) container operating on AWS Fargate uploads the file to Amazon Easy Storage Service (Amazon S3) within the Examgen bucket underneath the prefix exams.

1.4 The S3 bucket is configured utilizing occasion notification. At any time when a brand new file is uploaded, a PutObject is activated to ship the file to the ExamGenFn Lambda operate.

1.5 The Lambda operate ExamGenFn invokes the Anthropic Claude v2.1 mannequin on Amazon Bedrock to generate examination questions and solutions as a JSON file.

1.6 The Amazon Bedrock API returns the output Q&A JSON file to the Lambda operate.

1.7 The ExamGenFn Lambda operate saves the output file to the identical S3 bucket underneath the prefix Questions-bank. (You possibly can select to put it aside to a special S3 bucket.)

1.8 The ExamGenFn Lambda operate sends an e mail notification to the educator via the SNS matter to inform that the examination has been generated.

  1. The learner opens the touchdown web page to take the examination underneath the area take-exam.<your-domain-name> via Route 53, which redirects the request to the ALB.

2.1 The ALB communicates with Amazon Cognito to authenticate the learner on the learner consumer pool.

2.2 The learner accesses the frontend and selects a check to take.

2.3 The container picture sends the REST API request to Amazon API Gateway (utilizing the GET technique).

2.4 API Gateway communicates with the TakeExamFn Lambda operate as a proxy.

2.5 The Lambda TakeExamFn operate retrieves from S3 bucket underneath the prefix Questions-bank the out there examination in JSON format.

2.6 The JSON file is returned to API Gateway.

2.7 API Gateway transmits the JSON file to the ECS container within the front-end.

2.8 The container presents the examination as a UI utilizing the Streamlit framework. The learner then takes the exams. When the learner is completed and submits their solutions, the ECS container performs a comparability between the solutions offered and the right solutions, after which exhibits the rating outcomes to the learner.

2.9 The ECS container shops the scorecard in an Amazon DynamoDB desk.

2.10 The Lambda DynamoDBTriggerFn operate detects the brand new scorecard document on the DynamoDB desk and sends an e mail notification to the educator with the learner’s scorecard.

That is an event-driven structure made up of particular person AWS providers which might be loosely built-in with one another, with every service dealing with a particular operate. It makes use of AWS serverless applied sciences, permitting you construct and run your software with out having to handle your personal servers. All server administration is completed by AWS, offering many advantages equivalent to computerized scaling and built-in excessive availability, letting you’re taking your thought to manufacturing rapidly.


On this part, we undergo the prerequisite steps to finish earlier than you may arrange this resolution.

Allow mannequin entry via Amazon Bedrock

You possibly can add entry to a mannequin from the Amazon Bedrock console. For this walkthrough, it’s worthwhile to request entry to the Anthropic Claude mannequin on Amazon Bedrock. For extra data, see Mannequin entry.

Set up the required packages

You have to set up the next:

Register a DNS area and create certificates

In the event you don’t have already got a DNS area registered, it’s worthwhile to create one with a purpose to not expose the DNS of your ALB. For directions, discuss with Registering a brand new area.

You additionally must request two public certificates, one for every front-end: gen-exam.<your-domain-name> and take-exam.<your-domain-name>. Seek advice from Requesting a public certificates to request a public certificates on AWS Certificates Supervisor.

Save the values for genCertificateArn and takeCertificateArn.

If you wish to construct the app in a improvement setting with out utilizing your personal area, you may uncomment the next part within the sam template:

# un-comment if it's worthwhile to check with HTTP visitors and no certifcate
#  ExamGenALBHTTPListener:
#    Sort: AWS::ElasticLoadBalancingV2::Listener
#    Properties:
#      LoadBalancerArn: !Ref ExamGenALB
#      Protocol: HTTP
#      Port: 80
#      DefaultActions:
#        - Sort: ahead
#          TargetGroupArn: !Ref ExamGenTG

Chain-of-Thought (CoT) Prompting

Earlier than we embark on developing the app, let’s delve into prompt engineering. We use Chain-of-Thought (CoT) Prompting, which permits the mannequin to interrupt down advanced reasoning into smaller, extra manageable steps. By offering the AI with intermediate prompts that information its reasoning course of step-by-step, CoT prompting allows the mannequin to deal with subtle reasoning duties. Guiding the AI via an analytical chain of thought on this manner permits it to develop advanced reasoning capabilities that may in any other case be past its unaided skills.

Within the ExamGenFn Lambda operate, we use the next immediate to information the mannequin via reasoning steps. You possibly can change the immediate and provides it totally different personas and directions, and see the way it behaves.

template_instruction = f"""Human: 
You're a instructor throughout examination time and you're chargeable for creating examination questions from the coed examine ebook.
Earlier than creating the questions
- Analyze the ebook discovered between <exam_book> </exam_book> tags, to establish distinct chapters, sections, or themes for query era.
- For true/false questions, choose statements that may be clearly recognized as true or false based mostly on the ebook's content material.
- For MCQs, develop questions that problem the understanding of the fabric, making certain one appropriate reply and {n_mcq_options-1} distractors which might be related however incorrect.
- Randomize the choice of pages or matters for every run to generate a brand new set of questions, making certain no two units are equivalent.
Please present the questions on this format precisely for MCQ:
- The output ought to be like     
"query": "What's the color of the automotive within the ebook?",
"choices": ["Blue", "Green", "Yellow", "Grey"],
"correct_answer": "Yellow"
For True/False:
- the output ought to be like     
"query": "is the sky Blue?",
"choices": ["True", "False"],
"correct_answer": "True"
Generate {n_tfq} true/false and {n_mcq} multiple-choice questions (MCQs) making certain every query pertains to totally different pages or matters inside the ebook. For MCQs, present [n_mcq_options] choices for every query. Deal with creating distinctive questions that cowl a broad spectrum of the ebook's content material, avoiding repetition and making certain a various examination of the fabric. Use the next tips:
1. True/False Questions:
- Craft every true/false query based mostly on factual statements or key ideas from the ebook.
- Guarantee every query spans a variety of matters to cowl the ebook comprehensively.
2. A number of-Selection Questions (MCQs):
- Formulate every MCQ to evaluate understanding of great themes, occasions, or info.
- Embrace {n_mcq_options} choices per MCQ, ensuring one is appropriate and the others are believable however incorrect.
- Diversify the content material areas and pages/matters for every MCQ to keep away from overlap and repetition. 

Construct the examination generator software

The appliance introduced on this publish is on the market within the following GitHub repo with the constructing blocks code. Let’s begin with a git pull on the repo.

We advocate utilizing momentary credentials with the AWS CLI to make programmatic requests for AWS assets utilizing the AWS CLI.

Construct the front-end utilizing Streamlit and Docker

You construct two containers, one for producing exams and one for taking exams. Let’s begin with constructing the producing examination Docker picture:

  1. Go to the next path within the repo and construct your Docker picture:
consumer@exam-gen ~ % cd exam-gen-ai-blog/frontend/generate-exam-fe

consumer@exam-gen generate-exam-fe % docker construct -t <your-image-name>:tag .

  1. Authenticate the Docker CLI to Amazon Elastic Container Registry (Amazon ECR):
aws ecr get-login-password --region <your-region> | docker login --username AWS --password-stdin <your-account-id>.dkr.ecr.<your-region>

  1. Create a brand new repository in Amazon ECR:
aws ecr create-repository --repository-name <your-repository-name>

  1. Tag your Docker picture with the ECR repository URI:
docker tag <your-image-name>:tag your-account-id.dkr.ecr.<your-region><your-ecr-repository>:tag

  1. Push your tagged Docker picture to your ECR repository:
docker push <your-account-id>.dkr.ecr.<your-region><your-ecr-repository>:tag

  1. Navigate to this path within the repo to construct your Docker picture for taking the examination:
consumer@exam-gen ~ % cd exam-gen-ai-blog/frontend/take-exam-fe

  1. As a result of the authentication and the ECR repo are already executed, run immediately the next command:
consumer@exam-gen take-exam-fe % docker construct -t <your-image-name>:tag .

docker tag <your-image-name>:tag your-account-id.dkr.ecr.<your-region><your-ecr-repository>:tag

docker push <your-account-id>.dkr.ecr.<your-region><your-ecr-repository>:tag

  1. Copy the values for GenExamImageUri and TakeExamImageUri.

Now that you’ve got each containers able to run, let’s construct the remainder of the parts utilizing AWS SAM.

Construct resolution parts with AWS SAM

AWS SAM consists of two elements:

  • AWS SAM template specification – An open supply framework that you should utilize to outline your serverless software infrastructure on AWS
  • AWS SAM CLI – A command line device that you should utilize with AWS SAM templates and supported third-party integrations to construct and run your serverless purposes

For additional data, discuss with Utilizing the AWS Serverless Utility Mannequin (AWS SAM).

  1. Go to the house listing consumer@exam-gen ~ % cd exam-gen-ai-blog and run the sam construct command.

Earlier than you run sam deploy, pay attention to the next:

  • The ECS containers are deployed on Fargate, which wants a VPC with two subnets in several Availability Zones. We use the default VPC for simplicity. You possibly can create your personal VPC or use an present one in your AWS account and replace the sam template. To checklist your VPC IDs and subnets inside a specific VPC ID, run the next instructions to extract your VpcId and your two SubnetId:
aws ec2 describe-vpcs
aws ec2 describe-subnets

  • GenExamCallbackURL (for producing examination) and TakeExamCallbackURL (for taking examination) are utilized by Amazon Cognito. They’re URLs the place the consumer is redirected to after a profitable sign-in.
  1. Now let’s deploy the sam template:
sam deploy --stack-name <your-stack-name> --guided 
 --capabilities CAPABILITY_NAMED_IAM 

        #Reveals you assets modifications to be deployed and require a 'Y' to provoke deploy
        Verify modifications earlier than deploy [Y/n]: n
        #SAM wants permission to have the ability to create roles to connect with the assets in your template
        Enable SAM CLI IAM position creation [Y/n]: y
        #Preserves the state of beforehand provisioned assets when an operation fails
        Disable rollback [Y/n]: n
        Save arguments to configuration file [Y/n]: n

        In search of assets wanted for deployment:
        Creating the required assets...

        Efficiently created!

You possibly can observe the creation on the AWS CloudFormation console.

This following video demonstrates operating the sam construct and sam deploy instructions.

Determine 2: SAM construct and SAM deploy execution

  1. The ultimate step is to get the DNS names for the deployed ALB, map them to the certificates domains names in Route 53, and add them as a CNAME document.

Check the answer

You should use your browser to check the answer.

  1. Navigate to gen-exam.<your-domain-name>.

You’ll obtain an e mail with a affirmation code.

  1. Enter the verification code and select Verify account.

As soon as verified, you’ll land on a web page to generate your quiz.

  1. Select the quantity of a number of selection and true/false questions you need to generate, then select Browse information to add an enter file.

For this instance, we use the whitepaper AWS Cloud Adoption Framework: Safety Perspective as our enter file. We generate 4 multiple-choice questions and one true/false query.

  1. Verify your subscription to the SNS matter (you’ll obtain an e mail).

Then you definately’ll obtain an e mail confirming the examination has been generated.

  1. Change to take-exam.<your-domain-name>, and also you’ll discover the examination on the dropdown menu.
  1. Select the examination, then select Load quiz.

  1. Then you may take the examination and select Submit to show the outcomes.

The educator will obtain an e mail with the scorecard of the learner.

You’ve simply constructed a easy software that randomly generates questions and solutions from uploaded paperwork. Learners can take the generated exams and educators can obtain scorecards through e mail when exams are full. The mixing with the DynamoDB desk lets you retailer the responses on a long-term foundation.

Increasing the answer

There are a lot of potentialities to construct on prime of this and create a totally featured studying and testing software. One space of enlargement is importing a number of paperwork directly. As of this writing, customers can solely add one doc at a time, however assist for bulk uploads would enhance effectivity and make it simpler to work with massive units of supply supplies. Educators may very well be empowered to collect and add content material from numerous paperwork and web sites as supply materials for questions. This gives better flexibility in comparison with utilizing a single doc. Furthermore, with a knowledge retailer, they may view and analyze learner solutions through a scorecard interface to trace progress over time.

Clear up

It’s necessary to scrub up your assets within the following order:

  1. On the Amazon S3 console, empty the bucket by deleting any information and folders.
  1. On the AWS CloudFormation console, delete the stack.


On this publish, we confirmed how one can construct a generative AI software powered by Amazon Bedrock that creates examination questions utilizing lecture paperwork as enter to assist educators with an automatic device to constantly modernize quiz materials and enhance learners’ expertise. Learners will be capable to take the freshly generated examination and get the rating outcomes. With the capabilities of Amazon Bedrock and the AWS SAM, you may enhance educators’ productiveness and foster pupil success.

For extra data on working with generative AI on AWS for training use circumstances, discuss with Generative AI in training: Constructing AI options utilizing course lecture content material.

In regards to the Authors

Merieme Ezzaouia is a Options Architect at AWS devoted to the general public sector. She helps clients in training and sports activities flip their ideas into tangible options, develop new providers, and foster innovation. Past work, Merieme’s passions embrace gardening, touring the world, and studying.

Mohammed Reda is a Options Architect at Amazon Net Providers. He helps UK colleges, universities, and EdTech corporations undertake cloud applied sciences, enhance their academic choices, and innovate on AWS. Exterior of labor, Mohammed enjoys operating and watching cooking exhibits.

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