RAG demos do not survive the move to production
Chunking strategy, hybrid retrieval configuration, and evaluation pipelines are rarely addressed in early AI projects, causing production failures at enterprise query scale.
AI Platform Enablement
Azure OpenAI Adoption
Move beyond Azure OpenAI demos with MCT-led enablement covering production RAG architecture, Azure AI Foundry, Semantic Kernel orchestration, AI-102 certification, and enterprise responsible AI controls.
AI-102 Certification ReadinessMCT-Led DeliveryProduction RAG ArchitectureResponsible AI ControlsAzure AI Foundry Labs
Updated for Azure AI Foundry & Responses API (2025)
Why This Matters Now
The challenges that bring enterprise teams to this conversation
AI-102 pursued without implementation context
Certification without architecture training produces engineers who pass exams but cannot design a governed, production-grade Azure OpenAI deployment.
Responsible AI controls added as an afterthought
Content safety configuration, prompt injection defense, and data residency requirements block deployment approval when discovered late in the delivery cycle.
Strategic context: The gap between an Azure OpenAI demo and a governed production deployment is almost entirely a skills and architecture gap — not a platform limitation.
Capability Coverage
What the program covers
Azure AI Foundry & RAG Architecture
Design retrieval-augmented generation pipelines using Azure AI Foundry, Azure AI Search with hybrid retrieval, and chunking strategies tuned for enterprise document corpora.
Semantic Kernel & LangChain Orchestration
Build AI application orchestration layers using Semantic Kernel plugins and LangChain chains with tool calling, memory management, and multi-step reasoning patterns.
AI-102 Certification Readiness
Structured preparation for the Azure AI Engineer Associate (AI-102) exam combining certification content with production implementation context and hands-on labs.
Responsible AI & Content Safety
Implement Azure AI Content Safety, Prompt Shield, and responsible AI governance policies to meet enterprise security review and regulatory deployment requirements.
Azure AI Search Integration
Configure vector search, hybrid retrieval, semantic ranking, and index design for enterprise knowledge bases, document archives, and structured data retrieval.
Production Prompt Engineering Patterns
Apply chain-of-thought, few-shot, and structured output patterns to improve reliability, consistency, and measurability of LLM responses in production applications.
Delivery Approach
How we deliver this
01
Assess
AI Readiness & Architecture Review
Evaluate existing data infrastructure, identify RAG-ready document corpora, and assess team capability gaps across engineering, architecture, and product roles.
02
Design
RAG Architecture & Governance Plan
Design production RAG architecture including chunking strategy, index design, retrieval configuration, content safety controls, and deployment governance model.
03
Enable
AI-102 + Applied Program Delivery
Deliver AI-102 certification preparation, custom Azure OpenAI programs, and hands-on RAG labs with Azure AI Foundry and Azure AI Search in production-representative environments.
04
Adopt
Evaluation Pipeline & Responsible AI Deployment
Close with evaluation pipeline configuration using Azure AI Evaluation SDK, content safety rule deployment, and production deployment readiness review.
Capability Programs
Programs for this area
Proof & Perspectives
Implementation evidence and strategic context
Representative Healthcare Product Team
Product and engineering teams needed to evaluate AI and generative AI use cases while building implementation capability in a governed enterprise setting.
Azure OpenAIResponsible AIEnterprise Prototyping
Read engagement detailsAI & Data8 min read
RAG in Production: Architecture Decisions That Actually Matter
Retrieval-Augmented Generation (RAG) has become the dominant pattern for grounding enterprise LLM applications in proprietary data. Yet most organisations underestimate the architecture decisions required to move from a working demo to a production system that is accurate, cost-controlled, and auditable.
RAGAzure OpenAIAzure AI SearchLLM
Read insightReady to Begin
Start your Azure OpenAI Adoption program
Work with our team to design an enablement program matched to your team's readiness, platform priorities, and delivery timeline.
AI-102 Certification Readiness
MCT-Led Delivery
Production RAG Architecture
Responsible AI Controls
