# Enterprise AI Engineering Bootcamp > Source: https://sukruyusufkaya.com/en/training/kurumsal-yapay-zeka-muhendisligi-bootcamp > Updated: 2026-05-25T01:11:11.718Z > Level: advanced > Topics: Kurumsal Yapay Zeka Mühendisliği, LLM, RAG, Retrieval Engineering, Agent Sistemleri, Tool Calling, Context Engineering, Prompt Engineering, Evaluation Engineering, LLMOps, Observability, AI Security, Prompt Injection, Data Leakage, AI Governance, Deployment, Private LLM, Open Source LLM, Vector Database, Multimodal AI **TLDR:** An advanced hands-on AI engineering program for enterprises covering production-ready RAG, agent systems, evaluation engineering, LLMOps, security, governance, and deployment together. ## Açıklama Enterprise AI Engineering Bootcamp is an advanced, intensive, and hands-on program designed to help companies go beyond merely using AI tools and instead learn how to design, build, evaluate, govern, and operate secure, scalable, auditable, and production-ready AI systems at enterprise scale. The training combines the modern large language model ecosystem, retrieval-based architectures, agent systems, evaluation engineering, LLMOps practices, security layers, data boundaries, deployment models, and enterprise AI architecture into one integrated backbone. As a result, the program becomes a strong engineering capability program not for teams that only write prompts, but for technical, data, software, and digital-transformation teams that want to build real enterprise AI capability. Throughout the program, participants systematically learn the building blocks of LLM-based applications, model-selection strategies, the design logic from prompt engineering to context engineering, structured-output approaches, tool calling and function-calling patterns, the retrieval engineering layer, production-ready RAG systems, hybrid retrieval and reranking strategies, multi-step agent workflows, memory and planning approaches, human-in-the-loop patterns, LLM evaluation and regression-testing logic, observability and tracing practices, cost-performance optimization, security threats, prompt injection and data-leakage risks, how enterprise AI governance affects technical teams, and the end-to-end production architecture of the modern AI stack. This bootcamp responds directly to several urgent needs: organizations moving from pilot-level chatbots and demo experiments toward real production-grade AI systems; demand for RAG architectures that work with internal documents, SOPs, knowledge bases, technical documentation, and ticket history; growing demand for agent systems that can connect to multiple tools and run workflows; scaling bottlenecks caused by security, accuracy, cost, and traceability challenges; the need for data and software teams to work on the same system with a shared engineering language; and the requirement to approach AI initiatives not only through model selection, but also through lifecycle management, evaluation, and governance. A major differentiator of the program is that it does not reduce AI engineering to a single technical theme. The training is not just about model usage or prompt writing; it presents a holistic enterprise AI engineering approach in which product architecture, retrieval quality, agent security, output validation, tool orchestration, deployment strategy, monitoring, testing, cost optimization, and governance are addressed together. Participants see through examples the technical and organizational logic of moving from teams that build demos to teams that deliver production systems. By the end of the training, participants gain an engineering perspective that enables them to distinguish more clearly the architectural building blocks of enterprise AI systems, select the right AI solution pattern for a given business problem, design production-ready RAG and agent-based systems, make quality sustainable through evaluation and LLMOps thinking, incorporate security and governance layers into technical design, and move enterprise AI projects into production in a more conscious and disciplined way. ## Kazanımlar - Match the right architectural pattern to the right enterprise AI problem. - Design production-ready RAG architectures and make the decisions needed to improve retrieval quality. - Develop tool-using agent systems and approval workflows. - Build systems that measure quality and manage regression risk through evaluation engineering. - Integrate LLMOps, observability, security, and governance layers into technical design. - Develop a more mature engineering approach for moving enterprise AI projects from prototype to production.

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This bootcamp is designed for technical teams that do not want to leave enterprise AI initiatives at the prototype level and instead want to build secure, traceable, scalable, and production-ready systems that solve real business problems. At the center of the program is the modern enterprise AI stack: model selection, prompt and context design, retrieval layers, agent workflows, evaluation, security, LLMOps, deployment, and governance. As a result, the training teaches participants not merely how to use tools, but how to design systems, measure them, protect them, and operate them sustainably.

Throughout the bootcamp, participants learn how to distinguish which AI pattern is appropriate for which business problem. They see that not every problem requires fine-tuning, not every solution requires agents, not every RAG application works with the same retrieval strategy, and not every technical success means production success. For that reason, the program is designed not as a “tool tutorial” but as an “architectural decision-making” training. It presents an integrated framework that runs from the model layer to retrieval, from retrieval to agent workflows, from agent workflows to evaluation and observability, and from there to security and governance.

One of the strongest aspects of the bootcamp is that it brings together the four axes that companies need most today. The first is production-ready RAG and retrieval engineering. Participants learn chunking strategies, embedding logic, hybrid search, reranking, source grounding, and context assembly in the context of enterprise knowledge systems. The second is agent systems that use tools and execute multi-step workflows. Planning, memory, delegation, human-in-the-loop, and approval-workflow design are covered here. The third is evaluation engineering and LLMOps. Participants learn that it is not enough for a system to work; it must be managed in terms of quality, correctness, task success, regression, and observability. The fourth axis is security and governance. Prompt injection, tool abuse, data leakage, uncontrolled output, auditability, and safe-usage principles are treated as inseparable parts of system design.

The bootcamp also advances through technically deep but clearly business-relevant examples. These include enterprise assistants working on internal documents, technical-support knowledge systems, ticket- and SOP-focused RAG applications, agent scenarios with approval mechanisms, multimodal workflows that understand documents, operations assistants using tools, LLM applications with quality-evaluation layers, and the architectural impact of private and open-source model alternatives. As a result, participants not only understand the concepts by the end of the training, but also see concretely how to turn them into enterprise projects.

Another important differentiator of the program is that it addresses AI engineering not only from a developer perspective, but also from platform, security, governance, and product perspectives. Many AI initiatives fail in companies not because of technical insufficiency, but because of wrong use-case selection, inability to measure quality, deployment complexity, unclear data boundaries, security gaps, and weak ownership models. The training makes these bottlenecks visible and provides participants with a more mature end-to-end engineering perspective.

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