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Understanding search mechanics is key to forming an effective search strategy. Different search mechanics can be used effectively in various contexts. This section will help you explore how search tools interpret your queries and how to adapt your approach using different techniques. You’ll learn how to use Boolean operators to build precise searches, apply citation chaining to follow research conversations over time, and recognize how natural language processing (NLP) supports more conversational and AI-assisted searching. Together, these approaches will help you choose the most effective search strategy for your research needs.

Boolean searching

Boolean searching helps you to identify exact matches of keywords. You've likely done Boolean searching before - this is when you give a database a list of words that describes your topic, and the database returns sources with matches to those words.

Examples: Databases, LibrarySearch

Boolean searching is helpful for...

Comprehensiveness: You can be very comprehensive in your search as you have precise control over the concepts you're searching and how you combine them.
Transparency: There is a one-to-one comparison between what you input in your search and the output of he results. Transparency is high as you know exactly how the database is selecting the materials it shows you.
Repeatability: If your search terms stay the same, your results stay this same. This allows you to go back, perform the same search, and discover the same results.

Citation chaining

Citation chaining is a research strategy that helps you find sources that cite each other. Using this strategy, you follow citation trails and patterns to see how papers and ideas are connected. You can do backwards or forwards citation chaining.

Backwards: Looking at the bibliography of a source to see who the author cited.
Forwards: Looking at who has gone on to cite a source.

In LibrarySearch, clicking on "Articles citing this" and "Cited in this article" underneath your search results will allow you to go through this process with ease.

Screenshot 2025-10-22 at 12-57-56 LibrarySearch - ruha benjamin.png

Examples: LibrarySearch, Google Scholar

Citation chaining is helpful for...

Visualizing the scholarly conversation.
Surfacing connections and related papers/authors.

Natural language searching

A natural language search is a search technique that allows users to input queries using everyday language, like a question or a full sentence, instead of using keywords. This is the type of searching typically done using generative AI tools. GenAI search tools use natural language processing (NLP) to interpret the user's intent and context to provide results.

Examples: Microsoft Copilot, Scopus AI

Natural language searching is helpful for...

Exploratory browsing: Similar to how you might use Wikipedia when conducting research, natural language searching can be a helpful jumping off point for getting you started.

When using GenAI tools, it's key to remember that these are language processing tools, not language understanding.

Large Language Models (LLMs) are used to generate results for tools like MS Copilot. The main job of an LLM is to predict which words are likely to appear next in a given phrase or sentence, similar to the way that predictive text works on your phone. When generating text, an LLM evaluates the likelihood of each possible next word. For example, if the model generates the phrase "the cow jumped over the..." it then determines the probability of each word in its vocabulary coming next.

This process is repeated word-by-word until it predicts the segment is finished. Because each word is selected independently and with some randomness, slightly different outputs may occur each time.

This is why it is crucial to be critical of your results. Module 6 will go deeper into critically evaluating search locations and sources.

Adapted from: University of Toronto's Coursework and GenAI: A Practical Guide for Students

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