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Prompt Engineering Framework

PRJ-AWS-GAI-033

Systematic prompt optimization and A/B testing

~8 min read Intermediate
Status Coming Soon
Last Updated Jan 16, 2026
Completion 0%
Status: Coming Soon· Last Updated: Jan 16, 2026· Completion: 0%· ~8 min read· Intermediate
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Estimated Monthly Cost

~$60/mo on minimal config
Bedrock $35Lambda $8S3 $10CloudWatch $7
Business ContextInconsistent and suboptimal large language model (LLM) responses due to lack of …

The Problem

  • Inconsistent and suboptimal large language model (LLM) responses due to lack of systematic prompt design and iteration.
  • Manual and time-consuming process for evaluating prompt effectiveness and comparing different prompt variations.
  • Difficulty in tracking prompt performance metrics and identifying regressions in generative AI applications.

The Solution

  • Implements a structured framework for prompt creation and versioning, leveraging AWS Parameter Store for secure storage.
  • Automates A/B testing of prompts using AWS Lambda to orchestrate invocations against Amazon Bedrock and collect response data.
  • Provides real-time monitoring and analytics of prompt performance through dashboards built with Amazon CloudWatch.

Business Value

  • Reduces prompt optimization cycle time by 70%, from days to hours, accelerating LLM application development.
  • Increases LLM response quality by an average of 15% through data-driven prompt selection.
  • Achieves 99.9% accuracy in A/B test result reporting, ensuring reliable prompt performance insights.
  • Decreases operational costs associated with manual prompt management by 25% annually.

Risk Mitigation

  • Mitigates risks of biased or harmful LLM outputs through systematic testing and human-in-the-loop review processes.
  • Reduces the risk of prompt injection attacks by implementing secure prompt storage and retrieval mechanisms via AWS Parameter Store.
  • Ensures compliance with internal AI ethics guidelines by providing auditable prompt versioning and performance history.
GRC MappingNIST AI Risk Management Framework (AI RMF) v1.0: Govern 1.1, Map 2.2, Measure 3.…

Compliance Frameworks

  • NIST AI Risk Management Framework (AI RMF) v1.0: Govern 1.1, Map 2.2, Measure 3.1, Manage 4.3
  • ISO/IEC 42001:2023 (AI Management System): A.5.2, A.6.1, A.8.3
  • AWS Well-Architected Framework (Security Pillar): SEC01, SEC02, SEC03

Security Controls Implemented

  • Access Control: Granular IAM policies restrict access to AWS Parameter Store for prompt management.
  • Data Encryption: Prompts stored in AWS Parameter Store are encrypted at rest using KMS.
  • Logging and Monitoring: Amazon CloudWatch logs all Lambda invocations and Bedrock API calls for audit trails.
  • Input Validation: Lambda functions include validation logic to sanitize inputs before passing to Bedrock.
  • Least Privilege: Lambda execution roles are configured with minimal permissions required for Bedrock interaction and Parameter Store access.

Audit Evidence

  • CloudWatch logs detailing prompt execution, A/B test results, and performance metrics.
  • AWS Parameter Store version history for all managed prompts.
  • IAM policy documents demonstrating least privilege access for relevant services.
  • Code review reports for Lambda functions, including input validation and security best practices.

Regulatory Alignment

  • EU AI Act (Draft): Article 10 (Data governance and quality), Article 15 (Human oversight)
  • NIST SP 800-53 Rev. 5 (Security and Privacy Controls): AC-3 (Access Enforcement), AU-2 (Audit Events)
  • GDPR (General Data Protection Regulation): Article 5 (Principles relating to processing of personal data), Article 25 (Data protection by design and by default)

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Architecture Diagram

PRJ-AWS-GAI-033 Architecture

Technology Stack

Bedrock
Parameter Store
Lambda
CloudWatch
Prompts

Complete Documentation

Prerequisites

IAM Admin or PowerUser role
AWS CLI v2 configured
Terraform >= 1.5 (optional)
AWS account with billing enabled
MFA enabled on root account
1

Clone & Configure

Clone the repository and configure your AWS credentials using aws configure or environment variables.

aws configure --profile cloudguard
2

Review IAM Policies

Review and attach the required IAM policies to your deployment role. Ensure least-privilege access is applied.

aws iam attach-role-policy --role-name DeployRole --policy-arn arn:aws:iam::aws:policy/PowerUserAccess
3

Initialize Infrastructure

Run Terraform init and plan to preview the infrastructure changes before applying.

terraform init && terraform plan -out=tfplan
4

Deploy Resources

Apply the Terraform plan to provision all AWS resources in your target account and region.

terraform apply tfplan
5

Verify & Monitor

Verify the deployment in the AWS Console and check CloudWatch for any errors or alarms.

aws cloudwatch describe-alarms --state-value ALARM

Deployment Guide

Step-by-step instructions to deploy this mission

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Architecture Diagram

Visual representation of the system architecture

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Source Code

Complete source code and configuration files

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Video Tutorial

Watch the complete walkthrough video

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