Generative research is a new approach to research that uses artificial intelligence to generate results more relevant and more useful. It differs from traditional search, which simply provides a list of results, by offering a more immersive and interactive experience.
Generative search offers many benefits, including:
- An improvement in the precision of the results,
- A discovery of new information,
- An increase in creativity.
What is generative research?
La Generative search is based on the principles of machine learning and natural language processing. It uses complex algorithms to analyze data and generate results that meet the user’s needs.
Generative research can take many different forms. For example, it can:
- Generate document or website summaries
- Create data visualizations
- Offer personalized recommendations
- Generate creative content, such as stories, poems or songs
Some existing generative research tools:
- Google Search : Google recently launched a new feature of generative search called Search Hints. This feature uses artificial intelligence to provide personalized search suggestions based on the user's search.
- Microsoft Bing : Microsoft Bing also offers a generative search feature called Bing Visual Search. This feature uses artificial intelligence to generate data visualizations from search results.
- IBM Watson : IBM Watson is an artificial intelligence platform that can be used to create generative searches. For example, Watson was used to create an application that helps students learn code.
The benefits of generative search
Improved accuracy of results
La Generative search can improve the accuracy of results by taking into account context and language nuances. For example, if a user searches for information on “the health effects of air pollution”, the Generative research could offer results that are specific to their location or lifestyle.
Generative search can improve the accuracy of results:
- A search for restaurants in a given city might provide results that are located near the user or match their dietary preferences.
- A search for electronic products might come up with results that are reviewed by experts or are on sale.
- A search for upcoming events might provide results that are tailored to the user's interests.
Discovery of new information
La Generative search can help discover new information by exploring unknown areas or combining information from different sources. For example, if a user is looking for information on “music history,” generative search could offer results that explore lesser-known musical genres or little-known artists.
Examples of how generative search can help uncover new information:
- Researching current trends might offer results that come from unconventional sources, like social media or blogs.
- Research on a complex topic might provide results that are presented in a clear and concise manner.
- Research on a controversial topic might come up with results that present different points of view.
Increased creativity
La Generative research can boost creativity by generating new ideas and perspectives. For example, if a user is looking for information about “the future of education,” generative search could provide innovative ideas for rethinking learning.
How can generative research boost creativity?
- Research into a complex problem could offer novel solutions.
- Research on an abstract concept could offer original illustrations or metaphors.
- Research on a creative topic could suggest ideas for artistic or literary projects.
The limits of generative research
The risk of bias
La Generative search can be biased by the data it is trained on. For example, if a generative search is trained on a dataset of texts that are primarily written by men, it could produce results that are biased toward men.
Some examples of the risk of bias in generative research:
- Research on gender roles could provide results that reinforce sexist stereotypes.
- Research on scientific achievements might come up with results that underestimate women's contributions.
- Research on political perspectives might offer results that favor a certain ideology.
The need for quality data
La Generative research requires quality data to produce accurate and useful results. If the data is of poor quality, generative research could produce results that are inaccurate or misleading.
Some examples of the importance of quality data in generative research:
- Research into historical facts could produce erroneous results if it is based on unreliable sources.
- Research on economic statistics could produce biased results if it is based on old or incomplete data.
- Weather forecast research could produce inaccurate results if it is based on outdated weather models.
The future of generative research
Generative search has the potential to revolutionize the way we search for information. It could have a significant impact on various areas, such as education, business and health. For example :
- Generative inquiry could personalize learning based on the needs and interests of each student.
- Generative search could help companies identify new business opportunities and improve their decision-making.
- Generative research could help doctors diagnose diseases more effectively and find new treatments.
Generative search is a promising technology that has the potential to change the way we learn, work and live. It is still in development, but it already has the potential to revolutionize our world.
Google's generative search under test
Google announced in May 2023 the launch of a new generative search feature called Search Hints. This feature is currently being tested with a limited group of users.
Search Hints uses artificial intelligence to provide personalized search suggestions based on the user's search. For example, if a user searches for information on “how to make a cake,” Search Hints might offer suggestions such as:
- The necessary ingredients
- The steps of the recipe
- Demonstration videos
- Similar recipes
Search Hints is based on an artificial language model called LaMDA, which was trained on a massive dataset of text and code. LaMDA is able to understand the context of a search query and generate suggestions that are relevant and useful.
Search Hints is a first step toward more sophisticated generative search. Google plans to continue developing this feature and make it available to more users in the coming months.
Some examples of Search Hints
Here are some examples of how Search Hints can be used:
- A user looking for information about holidays in France could receive suggestions for destinations, activities and accommodation.
- A user searching for information about a business might receive suggestions for products, services, and competitors.
- A user searching for information about an event might receive suggestions for dates, locations, and prices.
Search Hints has the potential to revolutionize the way we search for information. It could help us find the information we need more quickly and easily, and discover new things.
Esteban Irschfeld, SEO Consultant at UX-Republic
