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Corporate technical training · RuneLab Academy
Build systems that answer from your company's knowledge and cite their sources.
Hands-on training in building end-to-end RAG systems that answer from documents and cite their sources. From data preparation to hybrid retrieval, your team builds a RAG service, then adds the evaluation and security layers it needs for production.
The demo works; production is harder. The training walks through the six stages of a RAG system one at a time and, at each stage, shows how to measure, find and fix the most common mistake.
Bringing in PDFs, wiki pages and project documents, parsing and cleaning them.
Common mistakeTables and headings are lost during parsing.
Splitting documents into meaningful chunks; adding fields such as source, date and department.
Common mistakeA table is split across two chunks, so the answer is incomplete.
Choosing the embedding model and vector database by data volume and language.
Common mistakeChoosing an embedding model that is weak on Turkish queries.
Hybrid retrieval that combines BM25 and semantic search; ordering the results with a reranker.
Common mistakeThe right chunk is found but never reaches the top ranks.
Generating the answer from the retrieved context, tying every claim to its source.
Common mistakeThe model adds information that is not in the sources.
Measuring retrieval and answer quality; protecting against prompt injection and data leakage.
Common mistakeQuality is never measured; users find the problems first.
Not every corporate knowledge problem is solved with RAG. The training teaches how to choose between the three approaches based on the shape of the problem.
The three are not mutually exclusive: RAG can run on a model fine-tuned for style and format, with prompt caching for repeated context.
For technical teams building enterprise search, document search or knowledge assistants. Working knowledge of Python is enough; LLM and RAG concepts are covered from the start.
Teams that will build enterprise search, a document assistant or a knowledge service.
Engineers who want to take an existing RAG prototype to production with measurable quality.
Data scientists who want to measure retrieval quality, design evaluations and improve results.
Leads who will make RAG architecture, cost and security decisions and guide their team.
Ten hands-on modules, from the basic pipeline to agentic RAG. The weight of each module is set by your team's level and goals.
Why RAG is needed and when it is preferred over fine-tuning; grounded answers with citations; the need for accuracy, freshness and traceability.
Ingesting PDF, wiki and markdown, parsing and cleaning; chunking strategies, metadata design; choosing an embedding model and a vector database.
The end-to-end flow from ingestion to answer; connecting retrieval results to the prompt, citing sources, and the role of retrieval in reducing hallucination.
A question-answering service with FastAPI, an LLM API and a vector database: document upload, embeddings, retrieval and generation.
Workshop Every participant stands up their own mini RAG service.
Prompt structures and structured output for RAG; measuring retrieval with recall@k and MRR and answer quality with faithfulness and LLM-as-judge; golden sets and regression tests.
Hybrid retrieval, reranking, multi-query and query rewriting; BM25 analyzers and stemming for Turkish; context compression and filtering; an introduction to GraphRAG.
How agents differ from classic RAG; tool calling, planning and multi-step knowledge access across several data sources.
External API and webhook integration, rate limits and error handling; human-in-the-loop approval at critical steps.
API-first services, latency and token cost; a guardrail layer against prompt injection and data leakage; document-level permission filtering during retrieval and role-based access control.
Designing, building and presenting a knowledge assistant that answers with sources from documents across different systems.
Output A working prototype that can be adapted to your organization's scenario.
At your office; the exercises are prepared around your infrastructure and use cases.
Live sessions for teams in different locations; can be split into half-days.
Conceptual modules online, workshop modules on-site; for large groups.
Every participant receives a RuneLab Academy certificate of attendance. On request, the training is run together with Boğaziçi University Lifelong Learning Center and the certificate is issued with BÜYEM endorsement.
This program is designed for corporate teams. For those who want a short introduction or to go deeper on one topic, we also have courses open to individual participants.
An introductory workshop that builds a RAG architecture from scratch and ends the day with a working document chatbot.
View course →A RAG system that understands tables and images as well as text, so you can talk to complex documents.
View course →Hybrid search, ColBERT, reranking, GraphRAG, self-correcting and agentic RAG; security and performance.
View course →RuneLab's products are built on the architecture taught in this training. A reference for teams building their own system, and a ready-made solution for those who want to start quickly.
A ready-made RAG assistant for your documents: every answer comes with its source page.
See the product →
GPU infrastructure that runs your RAG system on-premise; documents and answers never leave your network.
See the product →
Masks sensitive data in document chunks before they reach a cloud model, and routes each request to the right model.
See the product →RAG, short for Retrieval-Augmented Generation, means a language model first retrieves the relevant information from the organization's own documents and then bases its answer on those sources. The model is not retrained; as documents are updated, answers follow, and every answer can show its source.
Working knowledge of Python and knowing how to call an API are enough. LLM and RAG concepts are covered from the start in the first module; for experienced teams the program is weighted towards the advanced RAG and agentic RAG modules.
The program is shaped around the team's level and goals. A program focused on the basic pipeline and the mini RAG service takes two days; the full program with advanced RAG, agentic RAG, integration and the capstone project takes five.
Exercises use LangChain or LlamaIndex, embedding models such as BGE-M3, and Qdrant, Milvus, Weaviate, pgvector or FAISS; the options are compared for your use case. Evaluation uses RAGAS. If your organization cannot use cloud models, the exercises can also be done with open source models running on-premise.
No. Exercises use sample document sets we prepare; no real company data is entered into any tool. The capstone project is designed around your organization's scenario.
This training focuses on designing systems that answer from corporate knowledge with cited sources, and it applies to any model, in the cloud or on-premise. The Local LLM Deployment Training covers running the model itself on your organization's servers. Organizations whose data cannot leave their network can plan both programs together. Explore the Local LLM Deployment Training.
RuneDesk is a complete software product built by RuneLab, installed in your organization and ready to use right away. It comes with sign-in and permissions, an admin panel and source-cited answers; it connects to sources such as SharePoint, Confluence and SAP, to databases, and to channels such as Microsoft Teams or Slack; and it runs in the cloud or on-premises. The RAG training, by contrast, helps your team understand how such systems work, add RAG-based features to their own products and assess a RAG solution against the right criteria. Its aim is not to replace a ready-made product but to help your team make sound decisions in this area and build solutions for its own needs. RuneDesk for an enterprise document assistant, the training for your team's skills: the two paths complement each other.
Every participant receives a RuneLab Academy certificate of attendance. On request, the training can be run together with Boğaziçi University Lifelong Learning Center and the certificate issued with BÜYEM endorsement.
The training is delivered by experienced senior AI engineers and data scientists who run enterprise AI projects at RuneLab.
Yes. In a short call before the training we learn about your team's level, your document sources and the scenarios you want to solve, and prepare the modules and the capstone project accordingly.
We prepare a quote for your organization based on the number of participants, the format and how much the content is tailored. Fill in the form and we will get back to you within one business day.
Tell us the number of participants, your team's technical profile, and your preferred format and dates; we will get back to you within one business day.
