“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Advanced Prompt Engineering & LLM Applications
This program moves beyond basic prompting into advanced patterns for production LLM deployments. Participants build RAG pipelines, apply function calling and reasoning-oriented prompt patterns, and use structured evaluation frameworks to measure and improve output quality.
VNode ITeSAdvanced
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration2 days
FormatVirtual or On-site
LevelAdvanced
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Advanced Prompt Engineering & LLM Applications as an MCT-led VNode ITeS program for AI engineers and developers building production LLM applications. Typical duration is 2 days (Virtual or On-site). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
AI engineers and developers building production LLM applications
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
Deep dive into RAG patterns, advanced prompting, and building production-ready LLM applications with LangChain and Semantic Kernel.
Prerequisites
- Completion of Azure OpenAI Fundamentals or equivalent applied experience
- Python proficiency
- Basic understanding of REST APIs
Program Outcomes
Capabilities your teams will gain
- Design and implement RAG pipelines with Azure AI Search
- Apply advanced prompting: chain-of-thought, few-shot, function calling
- Evaluate LLM outputs systematically using scoring frameworks
- Build orchestrated AI workflows with LangChain or Semantic Kernel
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
- RAG architecture with Azure AI Search
- Advanced prompting patterns and function calling
- LangChain and Semantic Kernel orchestration
- LLM output evaluation and quality scoring
1Module 1
RAG Architecture and Azure AI Search
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Module 1
RAG Architecture and Azure AI Search
Design and implement retrieval-augmented generation pipelines with Azure AI Search, index configuration, and retrieval optimization for grounded enterprise applications.
- implement retrieval-augmented generation pipelines with Azure AI Search
- index configuration
- and retrieval optimization for grounded enterprise applications
2Module 2
Advanced Prompting Patterns
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Module 2
Advanced Prompting Patterns
Apply production-level prompting techniques including chain-of-thought, few-shot, function calling, and structured output for reliable LLM integrations.
- Apply production-level prompting techniques including chain-of-thought
- function calling
- and structured output for reliable LLM integrations
3Module 3
LangChain and Semantic Kernel Orchestration
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Module 3
LangChain and Semantic Kernel Orchestration
Build orchestrated AI workflows with LangChain or Semantic Kernel for multi-step LLM applications and agentic task execution.
- Build orchestrated AI workflows with LangChain or Semantic Kernel for multi-step LLM applications
- agentic task execution
4Module 4
LLM Output Evaluation
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Module 4
LLM Output Evaluation
Evaluate and improve LLM output quality using structured scoring frameworks, safety checks, and systematic rubric-based benchmarking.
- improve LLM output quality using structured scoring frameworks
- safety checks
- and systematic rubric-based benchmarking
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Advanced practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Equips AI developers to build reliable, cost-controlled LLM applications that reduce hallucination risk and scale to production.
- LangChain vs Semantic Kernel track based on team preference
- Fine-tuning module for domain-specific models
- Incorporate team's own domain data and use case into labs
Resources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
