The Ultimate Guide to AI Search Engines: What Are They and How To Use Them

Artificial Intelligence (AI) has revolutionized numerous fields, and search engines are no exception. AI-powered search engines leverage advanced algorithms and machine learning techniques to understand and process user queries in ways traditional search engines cannot. This comprehensive guide explores the landscape of AI search engines, their features, benefits, and the technology driving their capabilities.

What are AI-Powered Search Engines?

AI search engines utilize artificial intelligence technology to deliver more relevant and accurate search results. Unlike traditional search engines that rely heavily on keyword matching and basic algorithms, AI search engines incorporate natural language processing (NLP), machine learning models, and other AI techniques to comprehend the context and intent behind user queries. 

What Makes AI Search Engines Different?

• Natural Language Processing (NLP): AI search engines can understand and process natural language queries, making it easier for users to ask questions in a conversational tone.
• Machine Learning: These search engines continuously learn from user interactions, improving their accuracy and relevance over time.
• Personalization: AI-powered search engines can tailor search results based on individual user preferences and search history.

Explore the capabilities of AI search engines

Key Features of AI Search Engines

AI search engines come with a range of advanced features designed to enhance the search experience. Some of those possible features include: 

• Natural Language Queries: Users can input queries in natural language, making searches more intuitive.
• Conversational Responses: AI search engines can provide answers in a conversational manner, often mimicking human responses.
• Personalized Search: Results are customized based on the user’s past behavior and preferences.
• Instant Answers: AI search engines provide quick, concise answers to queries, often displayed at the top of the search results.
• Enhanced Features: Advanced search options like private mode, AI-powered chat features, and code snippets for technical questions.

Types of AI Search Engines

AI search engines can be categorized based on their functionalities and target users. Each type serves different purposes and utilizes AI to enhance the search experience in unique ways. Here’s a closer look at each type, with a focus on the academic search engine, Consensus.

General Search Engines

General search engines like Google Search and Bing are designed to cater to a broad audience, offering search results for a wide range of queries. These search engines use AI to improve search relevance, ensuring that users receive the most pertinent information based on their search intent.

AI Integration:

• Natural Language Processing (NLP): Allows the search engine to understand and process complex queries.
• Machine Learning Models: Continuously learn from user interactions to refine search algorithms.
• Personalization: Tailors search results based on user behavior, preferences, and search history.

Academic Search Tools

Academic search tools are designed to cater specifically to academic and research queries. They index scholarly articles, theses, books, conference papers, and other academic resources. One notable AI-powered academic search engine is Consensus.

Consensus is an AI-driven academic search engine that focuses on delivering consensus-based answers from academic literature. It aims to synthesize findings from multiple sources to provide clear, concise, and evidence-based answers to complex questions.

Key Features of Consensus:

• Evidence Synthesis: Combines results from various studies to present a unified answer.
• Relevance Ranking: Uses AI to rank search results based on their relevance to the query.
• Natural Language Queries: Allows researchers to ask questions in natural language, making it easier to find specific academic information.
• Summarization: Provides concise summaries of academic papers, highlighting key findings and conclusions.
• Source Credibility: Emphasizes high-quality, peer-reviewed sources to ensure the reliability of information.

AI-Powered Site Search

AI-powered site search tools are integrated into websites to improve their internal search capabilities. These tools help users find relevant content within a specific site, enhancing the overall user experience.

Key Features:

• Custom Search Algorithms: Tailored to the specific needs of the website.
• Faceted Search: Allows users to filter search results by various criteria, such as categories, tags, or date ranges.
• Instant Search Results: Provides real-time, instant search results as users type their queries.
• Personalization: Adapts search results based on user behavior and preferences.

How an AI Search Engine Like Consensus Works: A Step-by-Step Breakdown

Consensus is an advanced AI-powered academic search engine that leverages AI to deliver relevant academic research and insights. Here’s a detailed step-by-step breakdown of how Consensus operates: 

Step 1: Query Input and Processing

• User Query Input: Users begin by entering their search queries into Consensus. The queries can be single words, phrases, or complex questions. For example:

• • Single-word queries: “Avocados”
  • Related concepts: “Magnesium & Sleep”
  • Yes/No questions: “Does lack of sleep increase Alzheimer’s risk?”
  • Instructions for Consensus Copilot: “Discuss the various supplements that reduce nausea.”

• Query Optimization: Consensus optimizes these queries by removing unnecessary words like ‘what’, ‘is’, and ‘are’. This refined query helps in accurately matching the search terms with the relevant research papers.

Step 2: Data Corpus and Initial Search

• Access to a Vast Database: Consensus searches over a vast database of more than 200 million research papers sourced from Semantic Scholar. This extensive dataset spans across various scientific domains and is updated monthly to include the latest research.
• Vector Search and Keyword Search: Consensus uses a combination of keyword search and Vector search over the abstracts and titles of the research papers. Vector search, a technique grounded in AI, helps in identifying the most relevant documents by understanding the semantic relationships between words in the queries and the documents.

Step 3: Extracting Key Takeaways

• Custom Fine-Tuned Language Model: A custom fine-tuned language model runs over the entire corpus to extract the Key Takeaway from every paper. This model is specifically trained to identify and summarize the essential points from research papers, ensuring that the most critical insights are highlighted.

Step 4: Relevance Scoring and Ranking

• Combining Metadata for Relevance: The relevance of each paper is scored by combining multiple pieces of metadata, including:

• • Citation Count: How often the paper is cited by other researchers.
  • Velocity of Citations: The rate at which the paper is being cited recently.
  • Study Design: The quality and robustness of the study design.
  • Publish Date: The recency of the paper.

• Re-ranking Results: This relevance score, combined with the metadata, helps in re-ranking the search results to produce the top 20 most relevant papers for the user’s query.

Step 5: Consensus Copilot and Enhanced Interaction

• Consensus Copilot Integration: Consensus Copilot adds ChatGPT-type functionality to the search engine. Users can instruct Copilot to answer questions, draft content, create lists, and more. The responses from Copilot are grounded in scientific research, with each statement linked to the corresponding research paper.

Step 6: Summary, Consensus Meter, and Final Presentation

• Generating Summaries and Consensus Meter: For queries that require a yes/no answer or topic summaries, Consensus runs OpenAI’s GPT-4 large language model over the top results. This model produces:

• • Summaries: A simple one-sentence summary of the top studies related to the query, displayed in the Summary box.
  • Consensus Meter: Aggregated results indicating “yes,” “no,” or “possibly” based on the relevant research, shown in the Consensus Meter.

• Final Result Ranking: A final custom fine-tuned language model built for question answering ranks the results according to how well they address the user’s query. The top 20 results are then presented to the user, each linked to detailed research papers and summaries.

What are the Advantages of AI-Powered Search Engines?

AI-powered search engines offer significant advantages over regular search engines, enhancing the search journey for active users. By leveraging intelligent search capabilities, these engines provide more relevant content, key insights, and a personalized experience. Discover how AI transforms search functionality and delivers superior results in various contexts.

Relevance

AI search engines excel in delivering relevant search results by leveraging advanced natural language processing (NLP) techniques. Unlike traditional search engines that primarily rely on keyword matching, AI-driven search engines can comprehend the context and intent behind user queries. This means they can understand the nuances of language, such as synonyms, homonyms, and the overall meaning of a query, leading to more precise and relevant results.

User Experience

AI search engines significantly enhance user experience by offering personalized and conversational search interactions. By analyzing user behavior, preferences, and search history, AI search engines can tailor the search results to match individual needs and interests. This personalization makes the search process more intuitive and user-friendly.

Accuracy

One of the most significant advantages of AI search engines is their ability to learn and improve over time. Machine learning models power these search engines, allowing them to analyze vast amounts of data, identify patterns, and refine their algorithms continuously. This leads to higher accuracy in search results, as the search engine becomes better at understanding and predicting user needs.

Speed

AI search engines are designed to provide faster retrieval of information with instant and precise answers. By leveraging advanced algorithms and real-time data processing capabilities, AI search engines can quickly analyze queries and deliver relevant results almost instantaneously.

Get Started With Consensus Search 

Consensus is the ultimate AI-powered academic search engine designed to transform the research process. By leveraging advanced language models and vector search technology, Consensus scans over 200 million research papers to deliver the most relevant results.

It synthesizes topic-level and paper-level insights, providing clear, evidence-based answers linked directly to real research. With features like the Consensus Copilot, Key Takeaways, and the Consensus Meter, it offers an unparalleled, intelligent search experience tailored for academic excellence!

Start Searching Today