How to Build Production-Grade RAG in One Day
Transform RAG prototypes into enterprise-ready systems with advanced retrieval techniques, contextual embedding, and multi-stage orchestration
What is RAG?
Retrieval-Augmented Generation (RAG) combines the power of large language models with your own data. Instead of relying solely on the model's training, RAG retrieves relevant information from your documents in real-time and uses it to generate accurate, grounded responses.
Real-World Use Cases — Examples
These are illustrative examples of how RAG can be applied. In the workshop, you'll learn the techniques to build similar systems for your own use cases.
Education
A university builds an AI assistant that answers student questions using course materials, syllabi, and past lectures. Students get instant, accurate answers sourced from official course content, with citations to specific documents.
Public Administration
A city hall deploys a RAG system that helps citizens navigate bureaucratic procedures. The AI searches through regulations, forms, and procedures to provide step-by-step guidance for permits, applications, and legal requirements.
Software Development
A tech company creates an internal coding assistant that understands their entire codebase, architecture docs, and API specifications. Developers get instant answers about implementation patterns, with direct links to relevant code examples.
Why RAG?
RAG skills are among the most sought-after in the Romanian and international AI market in 2025
Accurate, Grounded Responses
Reduces AI hallucinations by anchoring to your data
Private Data Integration
Use proprietary documents without retraining models
Real-Time Updates
Change your data, change your AI's knowledge immediately
Source Attribution
Every answer traceable to specific documents
Domain Expertise
Transform any LLM into your domain expert
Real-Life Applications
Education, public administration, healthcare, legal, and more
What You'll Learn
Complete architecture of a production-ready RAG system, from document processing to orchestration
Advanced chunking techniques and contextualized embeddings for superior performance
Implementation of hybrid retrieval strategies and reranking
Optimization for latency and cost in enterprise applications
What You'll Take Home
Market-Ready Skills
Immediate competitive advantage in the expanding AI market
Professional Network
Connect with tech professionals and exchange practical experience
Functional Code
Working implementation ready to adapt to your projects
Official Certificate
Participation diploma signed by the trainer
Complete GitHub Repository
Full source code and checkpoints for future reference
OpenAI API Credits
$10 in credits to test your implementation
Workshop Agenda
Hands-on coding workshop with GitHub checkpoints for each session
Registration and Networking
Coffee, snacks, and meet fellow participants
Module 1: Foundation & Quick Start
- •Generative AI & RAG concepts overview
- •What we're building: Final system architecture walkthrough
- •Dataset introduction for the workshop
- •Hands-on: Build simple RAG in 15 minutes with Cloudflare AI Search
- •Test and explore limitations/caveats
Break 1
Coffee, snacks
Module 2: Advanced Document Processing
- •Better data = Better RAG
- •Smart chunking, contextualization, metadata extraction
- •Hands-on: Document-to-markdown, hierarchical chunking, context injection
- •Hands-on: Hybrid indexing with vectors + BM25 - implementing a retriever
Networking Lunch
Included in registration
Module 3: Intelligent Retrieval
- •Query understanding and prompt engineering
- •Query rewriting techniques
- •Hands-on: Build and test improved retriever
- •Reranking strategies and benefits
Break 2
Coffee, snacks
Module 4: Production & Scale
- •Hands-on: Agentic retrieval with tool integration
- •Observability and guardrails
- •Cost analysis, caching, optimization strategies
- •Open Q&A session
Conclusions and Closing
Final thoughts and next steps
Your Trainer
Timo Bejan
CTO & Co-Founder @ DevPlant
CTO, solution architect, and community builder with 15+ years delivering secure, cloud-native B2B platforms.
With a strong background in big data pipelines and distributed systems, Timo is now building next-gen AI systems for compliance, biotech, and automation.
Who Should Attend
Developers implementing AI search/chat for their organizations
Engineers looking to integrate LLMs with existing data
Technical leads evaluating RAG for production use
Anyone building AI applications that need accurate, current information
Technical Prerequisites
- Java or TypeScript understanding
- Basic experience with LLMs
What to Bring
- Laptop with minimum 16GB RAM, quad-core processor
- Docker installed
- TypeScript or Java development environment
- Git installed for accessing materials
What We Provide
Benefits for Companies and Teams
Investing in your team's Generative AI and RAG capabilities delivers measurable business impact
Gen AI & RAG Expertise
Build a common language and shared understanding of Generative AI and RAG systems across your organization
Faster Time-to-Market
Accelerate AI implementation in your products from months to weeks
Optimized AI Costs
Learn enterprise-grade optimization strategies that reduce operational costs by 40-60%
Production-Ready Skills
Move beyond prototypes to enterprise-grade systems with observability and guardrails
Competitive Advantage
Position your company at the forefront of AI innovation in your industry
Team Development Investment
Demonstrate commitment to professional growth and retain top technical talent
Group Participation & Corporate Customization
Looking to train multiple team members or need a customized workshop for your organization? We offer group discounts and can tailor content to your specific use cases and technology stack.
contact@banatit.roReady to Master Production RAG?
Limited to 20 participants — Secure your spot today
REGISTER NOW - €250 ONLYLunch and coffee/snacks included • November 27, 2025 • FABER Timișoara