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HIST 300 - Introduction to Historical Studies: Advanced Google Search vs. Artificial Intelligence (AI) Search

Understanding the Core Differences

Fundamentally, traditional search engines such as Google and Bing have operated using very different principles from an AI search engine, but to the casual observer it may be increasingly difficult to see how they differ. 

Traditional search engines operate in essentially the same way that library databases do, analyzing specific keywords entered by the user and matching those to text within websites, returning links that most closely match what the user requested.  These links are identified through a process called "crawling", where the search engine scans hundreds or thousands of websites, parsing the data to find keyword matches and then using indexing to rank the pages based on relevance. 

However, with the emergence of Artificial Intelligence (AI) and Generative AI systems and their increasingly common inclusion into traditional search engines such as Google, the lines between traditional search engine behavior and AI-enhanced search engines has become blurry.  This creates challenges for students in courses where their professor or teacher has forbidden the use of AI.  Knowing how these two types of searching and search tools are structured will help you identify the appropriate times to use them in your research.    

Traditional Search Engines

Advanced Google search: The traditional power-user toolkit 

(Use this link to navigate directly to Google's Advanced Search page: https://www.google.com/advanced_search)  

Advanced Search is a manual process that relies on specialized syntax, or "operators," to filter results from a pre-existing index of web pages. 

Characteristics:

  • Method: Matches keywords in your query with words found on web pages.
  • Query style: Requires prior knowledge of commands (e.g., site:, filetype:, using quotation marks to search for exact phrases).
  • Output: A list of blue links to relevant web pages and snippets.
  • When to Use:
    • Academic research: Limiting results to specific domains (.edu) or file types (.pdf).
    • Competitive business analysis: Searching a competitor's site for specific keywords.
    • Precise information retrieval: Finding an exact phrase or quote.
  • Limitations:
    • Requires the user to know the right commands.
    • Struggles with interpreting complex or conversational questions that don't match keywords.
    • Can be a time-consuming process of manually clicking through links to find the answer. 

Artificial Intelligence (AI) Search

AI-powered search vs. AI models (ChatGPT, Gemini, Copilot, etc.)

AI-powered search uses technologies like natural language processing (NLP) and large language models (LLMs) to understand the meaning and intent behind a query, not just the keywords. This concept has become a core component of modern search engines like Google and Bing. 

By contrast, AI models such as ChatGPT, Gemini, Copilot, and others can feel as if they operate similarly to traditional search engines, but the majority of these models do not yet have the capability to directly search the web.  Instead, they are trained on huge datasets and learn over time to spot patterns in the data to anticipate what a user may be requesting.

Characteristics of AI-powered search:

  • Method: Interprets natural language, analyzes context, and synthesizes information from multiple sources.
  • Query style: Conversational and intuitive; you can ask open-ended questions.
  • Output: Generates direct, summarized answers (often called "AI Overviews" in Google) and can support multimodal searches (image, voice, etc.).
  • When to Use (if allowed in your course):
    • Summarizing complex topics: Explaining a scientific concept without requiring the user to open and search multiple sites.
    • Making complex comparisons: Asking for a breakdown of economic policies between two political figures.
    • Conversational queries: Asking follow-up questions to refine a search.
  • Important Limitations of AI-powered search and AI models (Gemini, Copilot, ChatGPT, etc):
    • Hallucinations: AI can sometimes present inaccurate or "made-up" information as though it is authoritative.
    • Bias: The output can reflect biases present in the data that the model has been trained on.
    • Opaque decision-making: The process by which the AI model generates an answer is not always transparent.
    • Privacy concerns: Personalization relies on collecting user data.

 

Google's Blended Approach

The modern synergy: Google's blended approach

Google's current basic search integrates both advanced search functions and AI-powered features. This creates a powerful hybrid that allows for both precise and conversational searching.

  • AI Overviews: A key AI feature that provides a quick, AI-generated summary of search results at the top of the page for some queries.  This summary includes links out to the websites and pages used to generate the summary.  For academic use, students must still do additional research and fact-check the information.
  • AI Mode and Deep Search: Advanced AI features for Google AI subscribers that enable deeper, multi-step research by simultaneously searching across many sources.
  • Traditional results: Classic blue links still appear below the AI summaries, offering the opportunity for users to perform manual research and fact-check information.
  • Advanced operators: Search operators and the Advanced Search page remain available for users who want to apply specific, manual filters.

This blended approach means that for simple, factual, non-academic research queries, an AI summary is efficient. For complex, nuanced topics and inquiry, a user can start with an AI overview and then use the provided links and advanced search techniques to perform deeper, more reliable research. 

Constructing an Effective AI Prompt

In courses where you're allowed to use AI tools for specific purposes, you need to know what makes a prompt effective.  Much like database searching, if you structure your request poorly, you will not get effective results.  Multiple prompting frameworks exist that can help you to structure more effective prompts to AI models and save you time and frustration, with some better suited to general use such as the CARE Framework.    

THE CARE FRAMEWORK

CARE stands for:

  • C – Context: Provide background information on your search. The more context you give, the better the AI can tailor its responses.
  • A – Action: Clearly state what you want the AI to do. Be specific about the type of output, format, or task.
  • R – Rules: Set guidelines for how the AI should behave, including tone, style, length, or any constraints.
  • E – Examples: Offer examples of what good output looks like or reference formats the AI should follow.

By structuring prompts around CARE, you reduce ambiguity, improve relevance, and get outputs that are easier to implement.