Is this training suitable for non-technical participants?

Yes. The training is designed so that non-technical participants can follow it comfortably. Technical concepts are explained clearly, and the content can also be deepened for technical teams when needed.

Can the training be customized for a specific organization or industry?

Yes. The program can be customized based on industry, team profile, target use cases, current digital maturity, and organizational priorities. Department-specific examples can also be added when needed.

Do participants need software or Python knowledge?

No. Software knowledge is not required for the core learning path. However, if technical teams are included, more advanced examples and automation-oriented scenarios can also be incorporated.

What tangible outcomes do participants leave with?

Participants leave with example prompt sets, department-relevant use-case ideas, a more conscious AI usage framework, methods to improve output quality, and an initial post-training action plan.

Is this training focused only on ChatGPT?

No. The training is not limited to a single tool. It is structured around large language models, generative AI logic, and prompt engineering principles, making the acquired skills transferable across platforms and model families.

2 Gün

Introduction to Artificial Intelligence and Enterprise Prompt Engineering Training

This enterprise-focused training teaches AI foundations, large language models, prompt engineering, secure usage, and real business scenarios to help teams generate higher-quality and better-controlled AI outputs.

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About This Course

Detailed Content (EN)

This training provides a strategic starting point for organizations that want to adopt generative AI and large language models in a practical and sustainable way. Participants learn the foundations of how AI works, how LLM systems behave, what differentiates strong prompts from weak ones, how context influences output quality, and how these tools should be used safely in enterprise environments.

The program is not limited to theory. It also covers practical prompt patterns, role-based instruction design, document analysis techniques, structured outputs, transforming meeting notes into action items, report and email drafting, summarization, classification, and decision-support scenarios that can be directly applied to real business problems. As a result, participants move beyond experimentation and begin using AI more systematically in day-to-day workflows.

A major strength of the program is its explicit focus on security, governance, and quality. Topics such as data privacy, prompt injection awareness, hallucinations, bias, copyright, output verification, and enterprise usage boundaries are embedded into the learning experience so that organizations can scale AI more responsibly and effectively.

Training Methodology

Balanced program structure combining theory and hands-on practice

Enriched delivery with enterprise use cases and department-specific examples

Practical frameworks focused on prompt design, context management, and output quality

Business-value-oriented applications such as structured outputs, document analysis, and reporting

Security coverage including data privacy, prompt injection awareness, hallucinations, and reliability risks

Reusable prompt templates, governance principles, and internal adoption recommendations

Who Is This For?

Digital transformation teams and innovation leaders

Managers, team leads, and decision makers

HR, sales, marketing, operations, and customer experience teams

Business analysts, project managers, and product teams

Technical teams aiming to standardize AI usage at enterprise scale

Professionals who want to enter AI with a strong and structured foundation

Why This Course?

It positions AI not as a novelty, but as a business-value-generating capability.

It elevates prompt engineering from individual usage to enterprise maturity.

It helps participants generate higher-quality, more consistent, and better-controlled outputs.

It addresses security, privacy, ethics, and governance together with business value.

It enables fast post-training application through customizable enterprise examples.

It approaches AI adoption through problems and workflows rather than tools alone.

Learning Outcomes

Explain the foundations of AI, generative AI, and large language models.

Use role, context, task, constraints, and output format components more effectively to improve output quality.

Create practical prompts for business scenarios such as summarization, content generation, meeting-note transformation, reporting, and decision support.

Evaluate enterprise use cases such as document analysis, multi-source synthesis, and structured outputs.

Manage hallucination, data privacy, prompt injection, and reliability risks more consciously.

Build an initial prompt library and usage framework tailored to your own team or organization.

Requirements

Basic computer and digital tool literacy

Basic familiarity with workflows such as communication, reporting, or process operations

Technical background is not mandatory; the program is also suitable for non-technical participants

Participants are encouraged to bring example processes or problem areas from their own departments

Active participation is expected during hands-on workshops and scenario-based exercises

Course Curriculum

54 Lessons

Module 1: AI, Generative AI, and LLM Foundations9 Lessons

Module 2: Prompt Engineering Fundamentals and Effective Prompt Design9 Lessons

Module 3: Advanced Prompting, Context Management, and Structured Outputs9 Lessons

Module 4: Enterprise Use Cases, Document Analysis, and Multimodal Applications9 Lessons

Module 5: AI Security, Data Privacy, Ethics, and Governance9 Lessons

Module 6: Hands-on Workshop, Department Scenarios, and Organizational Adaptation9 Lessons

Instructor

Şükrü Yusuf KAYA

AI Architect | Enterprise AI & LLM Training | Stanford University | Software & Technology Consultant

Şükrü Yusuf KAYA is an internationally experienced AI Consultant and Technology Strategist leading the integration of artificial intelligence technologies into the global business landscape. With operations spanning 6 different countries, he bridges the gap between the theoretical boundaries of technology and practical business needs, overseeing end-to-end AI projects in data-critical sectors such as banking, e-commerce, retail, and logistics. Deepening his technical expertise particularly in Generative AI and Large Language Models (LLMs), KAYA ensures that organizations build architectures that shape the future rather than relying on short-term solutions. His visionary approach to transforming complex algorithms and advanced systems into tangible business value aligned with corporate growth targets has positioned him as a sought-after solution partner in the industry. Distinguished by his role as an instructor alongside his consulting and project management career, Şükrü Yusuf KAYA is driven by the motto of "Making AI accessible and applicable for everyone." Through comprehensive training programs designed for a wide spectrum of professionals—from technical teams to C-level executives—he prioritizes increasing organizational AI literacy and establishing a sustainable culture of technological transformation.

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