How to use AI for research

"How do I use AI for research?"

Generative AI can be an incredible starting point for background knowledge and research.

For example:

• Curate industry trends: Identify emerging opportunities and threats within your industry.
• Source expert opinions: Gather insights from experts on specific topics for informed decision-making.
• Research leads: Get a summary of a potential customer’s firmographics and market positioning.
• Source training materials: Identify and source materials for employee development programs.
• Compile best practices: Gather and organize best practices to enhance individual or team performance.
• Identify top sources: Quickly narrow in on the most famous sites, books, experts, etc. on a given topic.

… In other words, whenever you’re looking for a starting point to begin your research and need background knowledge, sources, or links to learn more.

✏️ Research-Related Starter Prompts

• What do you know about [general topic]? Specifically, what can you tell me about [subtopic], [subtopic], and [subtopic]?
• What are some of the most famous books on [topic], [topic synonym], or [related topic]?
• Explain [topic] at a 12th-grade level.
  • Now reduce the Lexile level by one and explain it again.
  • (Repeat as needed.)
• Summarize [topic] for a [describe audience].
• True or false: [topic] is a [your current understanding of it]?
• Identify the main arguments for and against [topic]. What are the strongest points on each side?
• Summarize the evolution of [field/concept] from [starting point] to present day. What have been the major milestones or paradigm shifts?
• Break down the process of [complex procedure] into its fundamental steps. Explain each step as if teaching it to a beginner.
• What are the potential implications of [recent event/development] on [industry/field]? Consider both short-term and long-term effects.

Make your research prompts as specific as possible. Use broad keywords (“Go To Market strategies”) along with specific questions, subtopics, synonyms, or examples (“GTM,” “inbound marketing,” “product-led marketing”).

Large Language Models are statistical predictors. LLMs are trained on vast amounts of text data, learning the relationships and probabilities between words. When you provide a broad prompt, the LLM has a wider range of statistically relevant words to choose from, which can lead to a more generic response.

Specificity refines the search space: With a specific prompt, you're guiding the LLM toward a smaller and more relevant section of its knowledge base.  Adding subtopics and examples acts like keywords in a search engine, focusing the LLM on the data that’s most likely to be useful and relevant to your request.

💡 More Tips

When using AI for research, remember:

• Use one of the frontier LLMs (i.e. GPT-4, Gemini, etc.). Bigger model = more training data = broader knowledge base and better results.
• Choose a tool that has web browsing capabilities (like ChatGPT, Gemini, or Perplexity) if you need the most up-to-date details or links to more information.
• Avoid leading questions like “Is it true that ____?” Some LLMs are more likely to provide answers that confirm rather than challenge the user’s assumptions, in their attempts to be helpful.
• Fact-check information by independently verifying with a trusted source before you share it.

📌 Want to Use Generative AI to Save Time?

Just remember RADIC:

• Research: To find or collect data on a topic.
• Analyze: To interpret or find patterns in data.
• Decide: To make a choice or recommendation based on data.
• Innovate: To combine or reimagine ideas in a new way.
• Create: To make something new — whether text, audio, video, image, or code.

RADIC is a simple framework that can help you to:

• Divorce your work from contexts like department, role, team, or industry.
• Break complex goals and responsibilities into smaller tasks.
• Consider the role of data in task completion. (What information is required to perform a given task?)
• Think about the degrees of sophistication and complexity in our work. (What do we do that’s routine and logical? What is more abstract and driven by intuition, instinct, or experience?)

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Question(s) to consider:

What's one area of your work where you feel you have the biggest blindspots? Could you use AI to begin to fill some of those gaps in your understanding?      

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