RAG demos work on happy-path queries but fail on enterprise long-tail
Regulatory references, named entities, and complex document lookups expose chunking and retrieval failures that only surface in production with real enterprise corpora.
AI Engineering Enablement
Prompt Engineering for Enterprise
Production-grade prompt engineering enablement — RAG architecture with Azure AI Search, structured prompting patterns, LLM evaluation pipelines, Semantic Kernel orchestration, and responsible AI guardrails.
Production RAG ArchitectureMCT-Led DeliveryLLM Evaluation PipelinesSemantic Kernel & LangChainResponsible AI Guardrails
Why This Matters Now
The challenges that bring enterprise teams to this conversation
LLM output quality is evaluated subjectively rather than systematically
Without evaluation pipelines and scoring metrics, production LLM reliability cannot be measured, guaranteed, or improved in a repeatable, auditable way.
Developers treat prompting as trial-and-error rather than structured engineering
Ad hoc prompting produces brittle, inconsistent results — structured engineering patterns are required for reliable, production-grade AI application output at enterprise scale.
Strategic context: In production enterprise AI, prompt engineering is the difference between an LLM that is reliable and one that is a liability — systematic patterns and evaluation are non-negotiable.
Capability Coverage
What the program covers
RAG Architecture with Azure AI Search
Design retrieval-augmented generation systems with hybrid search, semantic ranking, index chunking strategies, and query routing for enterprise document retrieval at scale.
Chain-of-Thought & Few-Shot Prompting
Apply structured prompting patterns — chain-of-thought, few-shot, self-consistency, and meta-prompting — to improve reasoning quality and output reliability in production.
Function Calling & Structured Output
Design function calling schemas, tool definitions, and structured JSON output patterns for LLM-integrated business applications with deterministic output requirements.
LLM Evaluation with Azure AI Evaluation SDK
Build systematic evaluation pipelines using Azure AI Evaluation SDK with groundedness, relevance, coherence, and custom metric scoring for production quality assurance.
Semantic Kernel & LangChain Orchestration
Implement multi-step AI application logic using Semantic Kernel plugins and LangChain chains with memory, tool routing, and agent patterns for complex AI workflows.
Responsible AI Guardrails & Prompt Injection Defense
Deploy Azure AI Content Safety filters, Prompt Shield protection, and systematic output review to prevent prompt injection and ensure compliant LLM output in production.
Delivery Approach
How we deliver this
01
Assess
LLM App Quality & Team Skill Baseline
Evaluate current LLM application quality, identify failure patterns in existing prompts, and assess team skill gaps across prompt engineering, RAG architecture, and evaluation.
02
Design
Scope Design by Team Level
Design program scope — fundamentals or advanced — based on team skill baseline, with specific application targets and evaluation criteria defined before delivery begins.
03
Enable
Structured Prompting + RAG Lab Delivery
Deliver structured prompt engineering programs and hands-on RAG labs covering chunking, hybrid retrieval, evaluation pipelines, and Semantic Kernel orchestration patterns.
04
Adopt
Evaluation Pipeline & Quality Monitoring
Close with evaluation pipeline configuration, systematic quality monitoring implementation, responsible AI guardrails deployment, and prompt injection defense in production.
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 Prompt Engineering for Enterprise program
Work with our team to design an enablement program matched to your team's readiness, platform priorities, and delivery timeline.
Production RAG Architecture
MCT-Led Delivery
LLM Evaluation Pipelines
Semantic Kernel & LangChain
