RAG — Building AI Applications with Your Own Data
A framework-agnostic Retrieval-Augmented Generation workshop from fundamentals to production: embeddings, vector databases, retrieval, evaluation, and observability — without locking into one framework or programming language.
RAG as architecture, not a single framework
Retrieval-Augmented Generation combines the power of language models with current, company-specific knowledge — without costly and time-consuming fine-tuning. Instead of teaching one specific stack (e.g. LangChain), the training walks through the architectural principles that govern RAG: how data reaches the index, how the system finds the right chunk, and how the model generates an answer grounded in the retrieved context. This knowledge transfers to any framework and programming language.
From chunking to reranking — the decisions that matter
RAG system quality is decided mostly at the data preparation stage, not in the model call itself. The workshop shows how chunking strategy and embedding model choice affect retrieval accuracy, how hybrid search combines semantic matching with keyword matching, and how reranking improves result quality before passing them to the model.
Evaluation as a condition for trusting a production system
A RAG system without systematic evaluation is a system that is hard to trust in production. Participants build a test set to measure retrieval accuracy and response faithfulness, and learn tools for monitoring (observability) a live system — so quality drops are caught before users notice them.
Benefits
- Design an end-to-end RAG architecture: from data source to model response
- Choose a chunking strategy and embedding model suited to your data type
- Compare and select a vector database for the scale and requirements of your project
- Build a process for evaluating RAG response quality and monitoring it in production
Who is this training for?
Prerequisites
- Basic programming knowledge (Python preferred, but not required to understand the architecture)
- Basic understanding of how large language models (LLMs) work — an introductory level is enough
Training program
RAG architecture — components and data flow
- Why RAG solves the problem of limited LLM knowledge and hallucinations
- The full pipeline: data indexing, retrieval, prompt augmentation, response generation
- When to use RAG, when to use fine-tuning, and when to combine both
- Overview of frameworks (LangChain, LlamaIndex) and the framework-free approach
Chunking and embeddings — the foundation of search quality
- Text chunking strategies: fixed-size, semantic, hierarchical
- Choosing an embedding model and its impact on retrieval accuracy
- Metadata and enriching chunks with source context
- Common data preparation mistakes that degrade RAG quality
Vector databases and retrieval
- Comparing vector databases: pgvector, Pinecone, Qdrant, Weaviate
- Hybrid search: combining semantic and keyword matching
- Reranking results and techniques to improve retrieval accuracy
- Scaling retrieval for large document volumes
Evaluation, observability, and production rollout
- RAG quality metrics: retrieval accuracy, response faithfulness
- Building an evaluation dataset for continuous testing
- Monitoring and observability of a RAG system in production
- Operating costs, caching, and performance optimization
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from RAG and LangChain, RAG in Practice, and Enterprise-Grade RAG in C# and .NET in our catalog?
Those three courses teach a specific framework or technology stack (LangChain, a production rollout starting from chunking, or a C#/.NET implementation). This training walks through framework-agnostic end-to-end architecture — embeddings, vector databases, retrieval, and evaluation — so participants understand the principles governing RAG and can apply them to any technology stack, not just one tool.
Is three days enough to build a production RAG system?
Three days is enough to work through the full architecture and build a working end-to-end prototype — from document indexing to a model response with source citations. Scaling to full production requires further work from your team, but participants leave with a ready architecture template and a technical decision checklist.
Which vector database should I choose for my project?
It depends on data scale, budget, and hosting requirements (self-hosted vs. managed). In the training we compare pgvector (a PostgreSQL extension, good for getting started), Pinecone and Weaviate (managed, scalable), and Qdrant (open source, high performance) — with concrete selection criteria for different scenarios.
How do I check whether my RAG system's answers are trustworthy and not made up?
We build an evaluation set with questions and expected sources, measure retrieval accuracy (did the system find the right chunk) and response faithfulness (does the answer stick to the retrieved context, without hallucinating). In the training we show tools and metrics for systematic, repeatable evaluation instead of manually checking individual examples.
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