Harnessing GenAI in Your Academic Journey

• GPT-4o, GPT-4o-mini
• Qwen2.5-72B-Instruct, Qwen2.5-VL-72B-Instruct
• Llama
• Mistral

• Copilot with Bing
• AI OneSearch Lite
• Scite Assistant
• Research AI in Statista
• Perplexity

• o1, o3-mini
• DeepSeek-R1
• Perplexity Pro Search
• Grok 3

• Handle open-ended and creative tasks better
• Versatile for simple reasoning tasks

• Combine generative models with external data retrieval, from databases or the internet 
• Provide more accurate, domain-specific or even up-to-date answers
• Reduce hallucinations 
• Responses may include verifiable sources for further fact checking

• Specialized generative models that prioritize logical reasoning over generative breadth
• Follow complex instructions and adapt to unfamiliar contexts more effectively
• Capable of breaking down complex tasks into manageable steps 

• Drafting outlines
• Brainstorming ideas
• Summarizing texts 
• Paraphrasing
• Translating

Excels at knowledge-intensive tasks, for example:

• Research Summarizing: Aggregate insights from multiple scholarly sources
• Fact-Checking: Allow the verification of claims with relevant references retrieved

• Complex reasoning, e.g. mathematical proofs, suggest experimental designs, evaluate methodologies, interpret statistical results, critical analysis etc. 
• Abstract or novel problems tackling, e.g. develops solutions for hypothetical scenarios, explores ethical dilemmas or thought experiments etc.

• Output Formatting: Define the desired structure for clear responses,
  e.g., “List in bullet points”, “Compare in a table”

• Structuring Complex Queries: Define contextual boundaries, such as assumptions, scope, or format to prevent overgeneralization 
• Exploring Alternatives: Trigger broader analysis by comparing options,
  e.g. “Compare two approaches to this problem”

• Role Prompting: Assign a specific role to guide the perspective,
  e.g. “Act as a history professor”, “Act as a data analyst”
• Iterative Refinement: Use follow-up prompts to tweak tone, depth, or focus,
  e.g. “Make the explanation concise”, “Expand on the second point”
• Task Decomposition: Break complex tasks into steps,
  e.g. “First define the term, then provide three examples”, “Outline the process before summarizing”

• Setting Constraints: Specify sources and time frames,
  e.g. “Use data from 2023 onwards”, “Retrieve data from PubMed only”
• Being Specific: Use precise queries to enhance retrieval accuracy,
  e.g. instead of “Explain climate change”, use “Identify three key mitigation strategies for climate change from recent studies”
• Encouraging Exploration: Prompt for gaps in knowledge,
  e.g. “What are unexplored research areas in …”, “Provide diverse perspectives on …”

Detailed and specific prompts can still boost performance

• Maintaining Individual Context Clarity: Avoid relying on conversational history, re-specify context in each prompt
• Being Specific: Use precise queries to enhance retrieval accuracy,
  e.g., instead of “Explain climate change”, use “Identify three key mitigation strategies for climate change from recent studies"

• Minimal zero-shot prompts can be sufficient
• Avoid few-shot prompts with many examples

• Focus on just the most critical information to prevent the model from overcomplicating its answer

• Prompt.chat by Fatih Kadir Akın
• 15 ChatGPT Prompts for Academic Writing by God of Prompt

• What is Retrieval-Augmented Generation (RAG)? by IBM Technology
• Prompt engineering with Retrieval Augmented Generation systems - tread with caution!  by Aaron Tay

• Prompt Engineering with Reasoning Models by Dan Cleary
• Prompt Engineering for OpenAI’s O1 and O3-mini Reasoning Models by Azure AI Services