[{"data":1,"prerenderedAt":63},["ShallowReactive",2],{"training:advanced-rag-kurumsal-llm-uygulamalari:en":3},{"id":4,"title":5,"slug":6,"summary":7,"cover_image_url":8,"category":9,"duration_hours":15,"level":16,"module_count":17,"description":18,"outline":19,"faq":46,"updated_at":62},132,"Advanced RAG & Enterprise LLM Applications","advanced-rag-kurumsal-llm-uygulamalari","With the widespread adoption of large language models (LLMs) in the corporate world, companies are now seeking to enhance general-purpose artificial intelligence with their own proprietary data to produce more accurate, up-to-date, and reliable results. The Retrieval-Augmented Generation (RAG) architecture fundamentally transforms response quality by reducing the risk of LLM hallucinations while pulling real-time information from sources such as corporate document repositories, knowledge bases, customer service records, and technical manuals. Today, numerous sectors—from banking to healthcare, from law to manufacturing—are placing the RAG architecture at the center of their efforts to develop internal knowledge assistants, customer support bots, document analysis systems, and decision support platforms.\n\nThe Advanced RAG & Enterprise LLM Applications training goes beyond the fundamentals of RAG logic to provide participants with hands-on experience in advanced approaches such as chunking strategies, embedding optimization, hybrid search, re-ranking, and multi-step query techniques. Through this training, enterprises can enhance their technical teams’ capacity to build production-ready, scalable, and secure LLM systems, shorten project development timelines, and derive tangible business value from their AI investments.","https:\u002F\u002Facademyclub.b-cdn.net\u002Ftrainings\u002Feab5d56fac3b415f979cc91d066fb9a6.webp",{"id":10,"name":11,"slug":12,"icon":13,"order":14},1,"Artificial Intelligence","yapay-zeka","pi-sparkles",0,null,"",3,"\u003Ch3>Training Content and Sample Curriculum\u003C\u002Fh3>\u003Cp>This training offers an end-to-end roadmap that starts from the fundamentals of RAG architecture and extends to building enterprise-scale, production-ready systems. The sample curriculum consists of the following topics:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Introduction to RAG Architecture and Core Components:\u003C\u002Fstrong> Working principles of retrievers, generators, and vector databases, and the limitations of classic RAG.\u003C\u002Fli>\u003Cli>\u003Cstrong>Chunking Strategies and Document Preprocessing:\u003C\u002Fstrong> Segmentation techniques and semantic partitioning methods tailored to different document types (PDF, Word, tables, code).\u003C\u002Fli>\u003Cli>\u003Cstrong>Embedding Models and Vector Database Selection:\u003C\u002Fstrong> Comparison of solutions such as Pinecone, Weaviate, Qdrant, and FAISS, and selection criteria based on enterprise needs.\u003C\u002Fli>\u003Cli>\u003Cstrong>Hybrid Search and Re-ranking Techniques:\u003C\u002Fstrong> Combining semantic search with keyword search, and applying cross-encoder-based re-ranking.\u003C\u002Fli>\u003Cli>\u003Cstrong>Multi-Step Query and Agentic RAG Approaches:\u003C\u002Fstrong> Breaking down complex queries into sub-queries, combining multiple sources, and agent-based information retrieval architectures.\u003C\u002Fli>\u003Cli>\u003Cstrong>Evaluation and Traceability:\u003C\u002Fstrong> Measuring RAG systems in terms of accuracy, consistency, and cost, using RAGAS and similar evaluation frameworks.\u003C\u002Fli>\u003Cli>\u003Cstrong>Security, Data Privacy, and Production Deployment:\u003C\u002Fstrong> Enterprise data security, access control, on-premise\u002Fhybrid deployment options, and cost optimization.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The curriculum for all our training programs is customized to suit your organization; please contact us to discuss your specific needs. The information provided here is a sample based on our previously delivered training programs.\u003C\u002Fp>\u003Ch3>Target Audience\u003C\u002Fh3>\u003Cul>\u003Cli>Software developers and backend engineers\u003C\u002Fli>\u003Cli>Data scientists and machine learning engineers\u003C\u002Fli>\u003Cli>AI product managers\u003C\u002Fli>\u003Cli>Solution architects and system designers\u003C\u002Fli>\u003Cli>Knowledge management and digital transformation teams\u003C\u002Fli>\u003Cli>R&D and innovation department staff\u003C\u002Fli>\u003C\u002Ful>\u003Ch3>Organizational Benefits\u003C\u002Fh3>\u003Cul>\u003Cli>Capacity to develop assistant systems that provide instant and accurate access to internal knowledge\u003C\u002Fli>\u003Cli>Increased reliability and customer trust through reduced LLM hallucinations\u003C\u002Fli>\u003Cli>Improved efficiency in document-intensive processes (legal, HR, customer service)\u003C\u002Fli>\u003Cli>Optimized development time and cost for AI projects\u003C\u002Fli>\u003Cli>Controlled LLM usage that complies with data security and privacy requirements\u003C\u002Fli>\u003Cli>Technical teams gaining competitive expertise in up-to-date RAG technologies\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Take your organization’s AI journey to the next level and prepare your team for the future with the Advanced RAG & Enterprise LLM Applications training; join this program, supported by hands-on workshops, and gain the knowledge and experience that will set your enterprise LLM projects apart.\u003C\u002Fp>",[20,30,38],{"title":21,"lessons":22,"duration":16},"RAG Architecture and Data Preparation",[23,24,25,26,27,28,29],"How Transformers and LLMs work","Purpose and architecture of RAG","Connecting LLMs with corporate documents","How embeddings work","How vector databases work","Chunking methods","Building a basic RAG pipeline",{"title":31,"lessons":32,"duration":16},"Advanced Retrieval and Evaluation",[33,34,35,36,37],"Semantic search","Metadata filtering","Multi-query retrieval","Separately evaluating retrieval and generation performance","Building a RAG test dataset",{"title":39,"lessons":40,"duration":16},"Secure Enterprise RAG",[41,42,43,44,45],"Testing LLM outputs","Causes of hallucination and mitigation methods","◦Handling unanswerable questions","Filtering sensitive and personal data","Exposing the RAG service as an API",[47,50,53,56,59],{"answer":48,"question":49},"In this training, we start with the core components of RAG architecture and cover chunking strategies, embedding models and vector database selection, hybrid search and re-ranking techniques, as well as multi-step query and agentic RAG approaches. We also include topics such as evaluating and monitoring RAG systems, along with security, data privacy, and production deployment. This way, participants move beyond basic RAG logic and gain hands-on experience with advanced approaches.","What topics are covered in the Advanced RAG & Enterprise LLM Applications training?",{"answer":51,"question":52},"This training is suitable for software developers and backend engineers, data scientists and machine learning engineers. AI product managers, enterprise architects and system designers, knowledge management and digital transformation teams, as well as R&D and innovation department staff can also benefit from this training.","Who is the Advanced RAG & Enterprise LLM Applications training suitable for?",{"answer":54,"question":55},"This training builds the capacity to develop assistant systems that provide instant and accurate access to internal organizational knowledge, while increasing reliability by reducing LLM hallucinations. It boosts efficiency in document-intensive processes such as legal, HR, and customer service, and optimizes development time and costs in AI projects. It also enables controlled LLM usage in line with data security and privacy requirements, and equips technical teams with competitive expertise in up-to-date RAG technologies.","What benefits does Advanced RAG training provide to an organization?",{"answer":57,"question":58},"RAG architecture fundamentally transforms response quality by reducing the risk of LLM hallucinations while pulling real-time information from sources such as corporate document repositories, knowledge bases, customer service records, and technical manuals. From banking to healthcare, and from law to manufacturing, many industries are placing RAG architecture at the center of developing internal knowledge assistants, customer support bots, document analysis systems, and decision support platforms.","Why has RAG architecture become so important for organizations?",{"answer":60,"question":61},"In this module, we cover testing LLM outputs, the causes of hallucinations and methods to mitigate them. It also covers handling unanswerable questions, filtering sensitive and personal data, and exposing the RAG service as an API.","What is taught in the Secure Enterprise RAG module?","2026-10-12T14:26:25.264934+03:00",1792015376403]