Prompt Engineering for Researchers
Master the art of prompt engineering to accelerate your research. Learn to leverage LLMs like ChatGPT, Claude, and DeepSeek for literature review, hypothesis generation, coding, and automation.
What You'll Learn
Practical prompt engineering skills to streamline your research workflow. Learn to get better results from AI tools without needing to write code.
Self-Paced
100% Online
Course Outcomes
By the end of this course, academic researchers will be able to:
Evaluate AI Capabilities and Limitations
Critically assess the architecture, capabilities, and inherent limitations of Large Language Models (LLMs)—including hallucinations, biases, and context windows—to ensure rigorous and responsible application in research.
Optimize Literature Reviews and Data Extraction
Apply advanced prompt engineering techniques (such as semantic filtering and audience personas) and AI search engines (like Perplexity) to efficiently screen academic literature, extract critical data, and synthesize complex scientific concepts.
Enhance Hypothesis and Proposal Generation
Utilize specialized prompt methodologies—including Chain of Thought, Flipped Interaction, and Few-Shot prompting—to rigorously structure experimental reasoning, formulate nuanced hypotheses, and develop comprehensive research proposals.
Streamline Academic Writing and Peer Review
Leverage Template, Outline Expansion, and Persona prompts to accelerate manuscript drafting (e.g., methodology sections), format data accurately, and conduct simulated peer reviews to strengthen argumentation and identify critical gaps.
Navigate AI Ethics and Academic Integrity
Navigate the ethical landscape of AI in academia—understanding authorship guidelines, AI detection tools, and data privacy—while establishing a framework to continuously integrate the latest AI advancements into the research workflow.
Course Content
Introduction to LLMs
Understand what LLMs are, the impact of ChatGPT, and key parameters. Includes hands-on practice with ChatGPT and Poe.com.
- Theory: Mechanics & Impact
- Practice: Basic Interaction
LLM Basics
Dive into prompt structure, engineering, and patterns. Learn about summarization, context windows, and audience personas.
- Theory: Prompt Patterns
- Practice: Persona Prompts
Prompt Engineering I
Master core strategies: Flipped Interactions, Few-Shot Prompts, and Chain of Thought reasoning for research proposals.
- Theory: Strategies & Logic
- Practice: Applying Techniques
Prompt Engineering II
Advanced techniques: Templates, Filtration, Outline Expanders, and Self-Reflection. Learn to combine prompts effectively.
- Theory: Advanced Patterns
- Practice: Complex Scenarios
AI Reasoning Modules
Explore AI reasoning with DeepSeek R1 and OpenAI o1. Learn "Vibe Coding" and accelerate literature reviews.
- Theory: Reasoning Models
- Practice: Vibe Coding
Automation & Final Remarks
Understand automation levels, limitations, and detection tools. Create automated workflows for articles and presentations.
- Theory: Ethics & Tools
- Practice: Workflow Automation
Meet Your Instructor
Dr. Mohamed Fadlalla
AI & Biomedical Specialist
A dedicated professional focusing on the intersection of AI and biomedicine. Passionate about teaching researchers how to utilize advanced computational tools to streamline academic workflows and data analysis.