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RAG Workshops

RAG Training: Enterprise Knowledge Base + LLM Workshops

Teaches you to build production-ready AI systems backed by company documents, databases and APIs using Retrieval-Augmented Generation (RAG).

TL;DR

One-line answer: RAG workshops — teach the Retrieval-Augmented Generation architecture end-to-end with vector databases, embedding models and LLMs, leading to production-ready systems grounded in enterprise documents.

  • Embedding models (OpenAI, Cohere, Voyage, open-source) and chunking strategies (semantic, fixed, hierarchical)
  • Vector databases: Pinecone, Weaviate, Qdrant, pgvector — selection criteria and performance benchmarks
  • Hybrid search (BM25 + vector), reranking, citation, evaluation (precision@k, MRR, NDCG)
  • Production: latency optimization, cost management, observability and hallucination guardrails

RAG Workshops — Programs

FAQ

What is RAG and why use it instead of fine-tuning?
RAG (Retrieval-Augmented Generation) automatically retrieves relevant enterprise documents and feeds them to the LLM, which then grounds its answer in that context. Three advantages over fine-tuning: (1) uses up-to-date data (no retraining needed), (2) provides citations (lower hallucination), (3) lower cost. RAG is the right choice for most enterprise use-cases.
Which vector database should I choose?
We teach the decision matrix in the workshop. Rule of thumb: small scale + existing Postgres → **pgvector** (operational simplicity). Cloud-native quickstart → **Pinecone**. Self-hosted + heavy filtering → **Qdrant** or **Weaviate**. High volume + cost sensitive → **Milvus**. Most Türkiye projects start with Qdrant or pgvector.
Why is chunking strategy so important?
RAG quality depends heavily on retrieval quality, which depends on chunking structure. Bad chunking (e.g. mid-sentence breaks, lost section context) can drop quality 30-50%. The workshop compares fixed, semantic, hierarchical and parent-document strategies and picks the one that fits your document type.
What is hybrid search and why is it needed?
Pure vector search is weak for some queries (e.g. specific product codes, dates, IDs). Hybrid search combines BM25 (keyword) + vector search + reranker. In production RAG, hybrid search almost always outperforms pure vector search. The workshop implements hybrid search hands-on on Qdrant/Weaviate.
What will I be able to do after the RAG workshop?
By the end you'll: (1) parse company documents and ingest into a vector DB, (2) select the right embedding model + chunking strategy, (3) build a hybrid retrieval + reranking pipeline, (4) return cited answers, (5) handle production deployment + monitoring (latency, cost, quality drift).