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View Course Programming with Large Language Models
Build production-ready AI applications with LLMs
3 days (21h)
Intermediate
On-site
Remote
Max 12 participants
Starting from
€1800
/person
- Pre-provisioned lab environment
- Course materials included
- Certificate of completion
- Post-course support
Custom quotes available for teams
Trusted By
Join professionals from leading companies who trained with us
Who Is This For
Who Is This For
This course is designed for:
- Software developers wanting to integrate LLMs into applications
- Technical leads evaluating AI capabilities for their products
- Data engineers building RAG pipelines
- Product teams prototyping AI-powered features
This course is NOT for:
- Those looking for ML/deep learning theory (this is application-focused)
- Non-technical roles without coding experience
What You'll Learn
What You'll Learn
By the end of this course, you will be able to:
- 1 Design effective prompts for consistent, reliable outputs
- 2 Build RAG (Retrieval-Augmented Generation) systems
- 3 Implement function calling and tool use patterns
- 4 Handle streaming, caching, and cost optimization
- 5 Deploy LLM applications to production safely
Prerequisites
Before attending, you should have:
- Proficiency in at least one programming language (Python, TypeScript, etc.)
- Basic understanding of APIs and web development
- Familiarity with ChatGPT or similar tools as a user
Course Outline
3-Day Course Outline
What you'll cover each day
Day 1 LLM Fundamentals & Prompt Engineering
6 topics • 7h
Day 1
LLM Fundamentals & Prompt Engineering
6 topics • 7h
Morning
- LLM landscape (GPT-4, Claude, open-source models)
- How LLMs work (without the math)
- API basics and authentication
Hands-on Lab: Set up development environment and make first API calls
Afternoon
- Prompt engineering principles
- System prompts and conversation design
- Output formatting (JSON mode, structured outputs)
Hands-on Lab: Build a reliable data extraction pipeline
Day 2 RAG & Vector Databases
6 topics • 7h
Day 2
RAG & Vector Databases
6 topics • 7h
Morning
- RAG architecture and components
- Embeddings and similarity search
- Vector databases (Pinecone, Weaviate, pgvector)
Hands-on Lab: Build a document Q&A system from scratch
Afternoon
- Chunking strategies and document processing
- Hybrid search (semantic + keyword)
- Evaluation and quality metrics
Hands-on Lab: Optimize RAG performance with real documents
Day 3 Advanced Patterns & Production
6 topics • 7h
Day 3
Advanced Patterns & Production
6 topics • 7h
Morning
- Function calling and tool use
- Agents and multi-step reasoning
- Error handling and fallback strategies
Hands-on Lab: Build an AI agent with tool access
Afternoon
- Streaming and real-time responses
- Caching and cost optimization
- Monitoring, guardrails, and safety
Hands-on Lab: Deploy a production-ready LLM application
Our Approach
Not Just Another Video Course
Every participant gets a pre-provisioned cloud environment with real-world tools and instructor assistance.
Real Environments
Work on actual cloud infrastructure, not sandboxed simulators. Kubernetes clusters, VMs, databases—all production-grade.
Remote Desktop Access
Your instructor can join your session instantly when you're stuck—see exactly what you see and guide you through.
Zero Setup Time
No "let's spend the first hour installing tools." Environments are pre-configured—you start learning immediately.
60% Hands-On Time
Most of each day is hands-on labs. The rest? Live demos, slides, and insider tips from your trainer.
training-lab-01
$ kubectl get pods -n training
NAME READY STATUS AGE
webapp-6d9f7b5c4-x2kl9 1/1 Running 2m
api-7f8d9c6b5-m3nj8 1/1 Running 2m
db-5c4d3b2a1-p4qr7 1/1 Running 2m
$ _
60%
hands-on practice
100%
real environments
Instructor can join your session
FD
CKA
CKAD
Your Instructor
Francesco Donzello
Lead Instructor & Founder
Francesco is a software engineer and certified Kubernetes administrator with over 10 years of experience building production systems. He founded Fraway in 2016 and has since trained 1000+ professionals across Europe in Kubernetes, Go, Flutter, and LLM development. His training approach emphasizes hands-on practice with real environments—not simulators—ensuring participants can apply skills immediately in their work.
CKA & CKAD certified instructor
1000+ professionals trained since 2016
Active practitioner building production systems
Delivered training at KPMG, Unicredit, Almaviva
1000+
Professionals Trained
10+
Years Experience
6
Countries
Testimonials
What Participants Say
FAQ
Frequently Asked Questions
Common questions about this course
01Which LLM provider do you use in the course?
Which LLM provider do you use in the course?
We primarily use OpenAI's API for consistency, but concepts apply to Claude, Gemini, and open-source models. We discuss trade-offs between providers and show examples with multiple APIs.
02Do I need my own API keys?
Do I need my own API keys?
We provide API credits for lab exercises. If you want to continue practicing after the course, you'll need your own accounts with your chosen providers.
03Is this course updated for the latest models?
Is this course updated for the latest models?
Yes. LLM capabilities evolve rapidly, and we update course content with each delivery to reflect the latest best practices and model capabilities.
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KPMG • Unicredit • Almaviva • Engineering