Planning and preparing for generative AI solutions
5 minutes
5 Questions
Planning and preparing for generative AI solutions in Azure involves several critical steps to ensure successful implementation. First, you must define clear business objectives and use cases. Identify specific problems that generative AI can solve, such as content creation, code generation, or con…Planning and preparing for generative AI solutions in Azure involves several critical steps to ensure successful implementation. First, you must define clear business objectives and use cases. Identify specific problems that generative AI can solve, such as content creation, code generation, or conversational interfaces. Understanding your requirements helps select appropriate Azure services like Azure OpenAI Service, Azure Machine Learning, or Azure AI Studio.
Next, assess your data readiness. Generative AI models often require grounding data to provide contextually relevant responses. Evaluate your data sources, quality, and accessibility. Consider implementing Retrieval Augmented Generation (RAG) patterns to enhance model responses with your organizational knowledge.
Security and compliance planning is essential. Review Azure's responsible AI principles and establish governance frameworks. Implement proper authentication using Azure Active Directory, configure role-based access control (RBAC), and ensure data privacy compliance with regulations like GDPR or HIPAA.
Resource planning involves selecting appropriate model deployments and estimating token usage. Azure OpenAI offers various models including GPT-4, GPT-3.5-turbo, and embedding models. Calculate expected throughput using Tokens Per Minute (TPM) and plan for quota management across deployments.
Architecture design should consider integration patterns with existing systems. Plan API endpoints, networking configurations including private endpoints if needed, and determine whether to use Azure AI Studio for orchestration or custom application development.
Cost estimation is crucial. Analyze pricing models based on token consumption and deployment types (standard vs provisioned throughput). Factor in storage costs for embeddings and vector databases if implementing RAG solutions.
Finally, establish monitoring and evaluation strategies. Plan for logging prompt-completion pairs, implementing content filters, and creating feedback loops for continuous improvement. Azure Monitor and Application Insights provide observability capabilities for tracking performance and usage metrics across your generative AI implementations.
Planning and Preparing for Generative AI Solutions
Why is Planning Generative AI Solutions Important?
Planning is the foundation of any successful generative AI implementation. Proper planning ensures that your solution meets business requirements, adheres to responsible AI principles, stays within budget, and delivers measurable value. For the AI-102 exam, Microsoft emphasizes that architects and engineers must understand how to evaluate, design, and prepare for generative AI deployments before writing any code.
What is Planning for Generative AI Solutions?
Planning for generative AI solutions involves several key activities:
1. Identifying Use Cases Determine which business problems are suitable for generative AI. Common use cases include content generation, summarization, code assistance, customer service automation, and knowledge extraction.
2. Selecting the Right Model Azure OpenAI Service offers various models including GPT-4, GPT-3.5-Turbo, and embeddings models. Each has different capabilities, token limits, and pricing. GPT-4 offers superior reasoning but costs more, while GPT-3.5-Turbo is faster and more economical for simpler tasks.
3. Capacity and Quota Planning Azure OpenAI has rate limits measured in Tokens Per Minute (TPM) and Requests Per Minute (RPM). You must request appropriate quota based on expected workload and choose between Standard and Provisioned Throughput deployments.
4. Data Preparation For solutions using Retrieval-Augmented Generation (RAG), you need to prepare your data sources, create embeddings, and configure vector stores like Azure AI Search.
How Does the Planning Process Work?
Step 1: Requirements Gathering - Define expected input and output formats - Establish latency requirements - Determine throughput needs - Identify data sources for grounding
Step 2: Architecture Design - Choose between Azure OpenAI standalone or integration with Azure AI Studio - Design the prompt flow and orchestration layer - Plan for content filtering and safety measures - Consider using Azure API Management for governance
Step 3: Cost Estimation - Calculate expected token consumption - Compare Standard vs Provisioned Throughput pricing - Factor in storage costs for embeddings and indexes
Step 4: Security and Compliance Planning - Implement managed identities for authentication - Configure private endpoints for network isolation - Plan for data residency requirements - Enable diagnostic logging for auditing
Step 5: Responsible AI Considerations - Configure content filters appropriately - Implement human oversight mechanisms - Plan for bias detection and mitigation - Document transparency measures
Exam Tips: Answering Questions on Planning and Preparing for Generative AI Solutions
Tip 1: Know Your Models Understand the differences between GPT-4, GPT-4 Turbo, GPT-3.5-Turbo, and embedding models. Questions often ask which model is appropriate for specific scenarios based on context length, accuracy, or cost requirements.
Tip 2: Understand Deployment Types Standard deployments use pay-as-you-go pricing with shared capacity. Provisioned Throughput Units (PTUs) guarantee dedicated capacity for predictable, high-volume workloads. Know when each is appropriate.
Tip 3: Remember Token Limits Be familiar with token limits for different models. GPT-4 Turbo supports 128K tokens, while GPT-3.5-Turbo supports 16K tokens. This affects document processing capabilities.
Tip 4: Focus on RAG Architecture Many questions involve Retrieval-Augmented Generation. Understand how Azure AI Search, embeddings, and vector indexes work together to ground model responses in your data.
Tip 5: Security First When questions present multiple valid approaches, prefer answers that use managed identities over API keys, and private endpoints over public access.
Tip 6: Cost Optimization Scenarios For cost-related questions, remember that smaller models and shorter prompts reduce costs. Caching responses and using system messages efficiently can also lower expenses.
Tip 7: Content Filtering is Default Azure OpenAI includes built-in content filtering. Know that you can adjust filter severity levels but cannot completely remove safety measures for most categories.
Tip 8: Watch for Quota Keywords Questions mentioning rate limiting, throttling, or capacity issues typically have answers involving quota increases, Provisioned Throughput, or load balancing across regions.