Course Objective
Participants receive an official certificate approved by Boğaziçi University Lifelong Learning Center (BÜYEM). This program is developed in partnership with Boğaziçi University BÜYEM and taught by Asst. Prof. Hüseyin Oktay Altun and Engin Deniz Alpman.
Prompt engineering is a critical capability that directly determines the quality of communication with AI systems and the business value of their outputs. This program equips participants with a strategic and engineering-grade approach to designing effective prompt architectures for complex workflows.
Participants master how large language models interpret instructions, context engineering, structured output control, and advanced reasoning patterns. Through hands-on n8n workflow automation labs, participants integrate LLMs, RAG pipelines, and multi-agent architectures directly into real business processes.
Target Audience
Professionals and managers actively using AI tools in daily operations
Content creators, marketing specialists, and HR professionals
Data scientists and AI / machine learning engineers
Product managers and business analysts
Digital transformation leaders and operations executives
Learning Outcomes
Design high-reliability prompts for complex enterprise tasks
Integrate LLMs into end-to-end business workflows using n8n
Build multi-agent architectures and RAG-backed knowledge assistants
Apply Chain-of-Thought, Tree-of-Thoughts, and self-consistency reasoning techniques
Create reusable prompt templates, standardized prompt libraries, and output validation pipelines
RuneLab ⨯ BULLC
Certificate of participation and achievement issued by Boğazici University Lifelong Learning Center upon course completion.
Curriculum
Foundations & Core Principles of Prompt Engineering
How large language models interpret instructions and the anatomy of an effective prompt
Clarity, precision, context management, zero-shot prompting, and few-shot learning
Advanced Reasoning & Structured Output Control
Chain-of-Thought, Tree-of-Thoughts, and self-consistency verification techniques
Structured response schemas, constraint-based generation, multi-document analysis, and meta-prompting
Interactive Customer Support Bot & Applied Scenarios
Building an information-gathering customer service bot and decision-support assistant
CRM integrations, data transformation, and end-to-end process automation
Enterprise Prompt Frameworks & Quality Assurance
Designing reusable prompt templates, standardized prompt libraries, and verification workflows
Business Applications & Comprehensive Case Studies
Marketing automation, customer operations, and data analytics workflows
Advanced Automation with n8n, RAG & Multi-Agent Systems
Workflow design, debugging, and API integrations with OpenAI, Claude, and Gemini in n8n
Architecting Retrieval-Augmented Generation (RAG) and multi-agent AI systems
Other Courses in This Category
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Prompt Engineering Fundamentals
Foundational course teaching structured prompt anatomy, zero-shot and few-shot patterns, constraint design, and iterative refinement to get predictable, high-quality outputs from enterprise LLMs.
- Live Course
- 4 modules
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Applied Prompt Engineering for Business Workflows
Hands-on intermediate course focused on solving real corporate workflows through task decomposition, structured data extraction, context integration, and reusable prompt templates.
- Live Course
- 4 modules
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Advanced Prompt Engineering and Reasoning Architectures
Advanced course covering production system prompt architecture, complex reasoning patterns (ReAct, self-correction, decomposition), tool-use instructions, and cross-model optimization.
- Live Course
- 4 modules
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Persona, Instruction, and Structured Output Templates
Practical course on designing modular corporate templates that combine domain personas, strict operational instructions, and deterministic output schemas (Markdown, tables, JSON).
- Live Course
- 4 modules
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Multi-Step Prompt Chaining and AI Workflows
Intermediate course on decomposing complex corporate processes into sequential, branching, and review-gated prompt chains that deliver reliable end-to-end outputs.
- Live Course
- 4 modules
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Context Engineering for Enterprise AI
Comprehensive course on designing, curating, structuring, and budgeting the information environment fed into LLMs—covering context rot prevention, token budgeting, and dynamic context assembly.
- Live Course
- 4 modules