Perplexity’s popularity has skyrocketed since its launch, but what is driving this adoption? Why have individuals and businesses alike found it so valuable? Today, we’ll explore how the platform works, how its hybrid nature differs from AI chatbots and search engines, and what limitations it has.

What is Perplexity AI?

Founded in late 2022, Perplexity AI is an AI-powered search and answer engine designed to provide conversational responses to user queries, complete with source citations. Perplexity’s aim is to revolutionize the traditional search experience by generating digestible, well-sourced answers to questions instead of a list of links. This results in more direct answers for users, source citations to the publishers of the original information, and more efficient research abilities based on current data.

We’re not going to cover the general concept of what AI is in this article, but if you’d like to take a step back before diving into the specifics of Perplexity, check out the link below:

Learn more about how generative AI and other types of AI work here.

What are the Main Features of Perplexity?

Perplexity has a few core functionalities that the platform is known for:

• Answer Engine: This is the central piece of Perplexity, which uses LLMs to answer user questions with inline citations.
• Deep Research: A more advanced research mode that uses reasoning and iterative search to refine its responses about a subject.
• Profiles: Users can create multiple profiles to save search history and certain preferences/settings.
• Follow-Up Questions: Once it has answered a query, Perplexity will suggest related questions to further guide user exploration deeper into a subject.

How to Use Perplexity for Search

Perplexity makes it easy to search web results for relevant information on a topic. Start by entering your question or search term into the search bar on Perplexity’s website or mobile app. We recommend simply using natural language (like how you would speak to a person) and being more descriptive to narrow the responses. In other words, instead of searching “coffee brew methods” like you might on Google, try “What is the best method for brewing coffee at home, considering flavor and ease of use?”

Perplexity then retrieves relevant web results, processes them, and generates a concise answer based on these findings. You can review the answer and check the sources to verify that it provided the information you’re looking for, asking follow-up questions if necessary. Users can also use Focus Mode to narrow results by a specific domain (such as just Reddit threads), or activate Copilot for deeper explorations of more nuanced queries (available on Perplexity Pro and Enterprise plans).

How Does Perplexity Compare to Traditional Search Engines?

With this new approach to search, you might be wondering how Perplexity compares to established search engines like Google and Bing. As we’ve illustrated, the primary areas of difference are information retrieval and presentation:

To summarize, Perplexity is often more efficient for direct knowledge queries, while traditional search engines are more helpful for product searches, local services, and other comparison-based information gathering. Perplexity prioritizes directness, while search engines like Google offer a broader landscape of information.

How to Use Perplexity for Research

The platform is increasingly being used for research as well, especially since the introduction of features like Deep Research. We recommend leveraging Perplexity’s research strengths by using it for complex questions like “What are the impacts of quantum computing on cybersecurity in the next 50 years, considering socioeconomic factors and potential mitigation strategies?” You can also refine the citations to prioritize scholarly sources and ensure that the information you receive is based on credible and peer-reviewed work.

Perplexity’s Deep Research has made this even more powerful, conducting more extensive searches across a wider range of sources for more nuanced answers. Pro or Enterprise users can also engage with Copilot to refine research questions, identify key concepts, explore different perspectives, and more. However, you should always critically evaluate its sources — look for peer-reviewed publications, reputable institutions, and well-established subject experts. Research is often an iterative process, so make use of Perplexity’s guided follow-up questions to learn more about the topic you’re investigating.

How Does Perplexity AI Work?

Before we get into the specifics of Perplexity’s inner workings, we’ll set the foundation that it still broadly follows a similar process to other LLMs. It tokenizes/processes user queries to identify intent, retrieves relevant information from training data, generates an answer based on context, and refines that answer for clarity and coherence. However, Perplexity’s focus on searching the web and providing citations alters this process compared to standard chatbot-focused AI like ChatGPT (more on this later):

1. Query Processing

The first stage of Perplexity’s process is understanding the user’s input. It does this by tokenizing (breaking up) the query and using NLP methods to tag each token (noun, verb, adjective, etc.). It then applies semantic understanding rules to identify entities, locations, concepts, intent, and areas where something might have multiple interpretations. Based on the intent and key terms Perplexity identifies, it may reformulate the original input into a more effective search query that still matches the user’s intent (adding synonyms, boolean operators, and so on).

2. Information Retrieval

Once the system “understands” a question and the input has been processed and formulated, the information retrieval system will begin searching a vast index of web content. Similar to Google’s index of crawled pages, Perplexity uses this type of system for efficient retrieval of relevant documents. Semantic search methods are executed to find these relevant documents, even if they don’t contain the exact term that the prompt uses. Then, based on relevance, content quality, source credibility, and more, Perplexity selects the top sources to answer the user’s question.

3. Answer Generation

From there, the selected documents and content are passed into an LLM to generate a natural language response based on the data contained in them. It uses context to identify and extract key pieces of information (such as facts, opinions, arguments, and evidence), summarizing them into a concise and focused answer. As this is being generated, the model keeps track of where each piece of information came from, attaching inline citations back to the original sources for each statement. This stage also resolves contradictions, enforces a more neutral tone, and involves several other sub-processes to deliver unbiased, fact-based responses.

4. Refinement

Post-generation, Perplexity will fact-check the answer and evaluate its coherence, completeness, and possible follow-up questions. These checks help ensure a more accurate response that is easy for the user to understand, fully addresses their initial question or prompt, and helps guide further exploration that might interest them.

How Does Its Contextual Memory Work?

Perplexity maintains contextual memory within a single conversation session. This means that users can ask follow-up questions that build on top of previous exchanges, without needing to repeat the entire context again and again. However, Perplexity currently does not have long-term memory that extends beyond individual sessions, like ChatGPT or other chatbots sometimes offer.

Nonetheless, Perplexity’s contextual memory is built on storing the current session’s conversation history (including user inputs and its own responses). When a new query is received, the system encodes relevant parts of the conversation history on top of the new query for a more contextual representation of what the user is asking for. The language model does this by using attention mechanisms to weigh the importance of various pieces from the conversation history. This contextual representation is then used to inform the successive steps of query processing, information retrieval, and answer generation outlined above.

How Does Perplexity Ensure the Accuracy of Its Responses?

A major hurdle of current LLMs is information hallucination, where they generate responses that include false “facts,” so how does Perplexity mitigate this issue? Some methods are simply inherent factors of Perplexity’s answer format (such as inline citations), which tie each piece of information to a real source.

However, not every source on the internet is trustworthy or up-to-date. Because of this, Perplexity uses real-time retrieval to find current information and typically relies on multiple sources to corroborate claims or data. Using fact-checking to compare generated information against other reliable data further refines the accuracy of responses, as does increased priority of known reputable sources like academic institutions, government agencies, and well-established news organizations. The platform also takes user reports of inaccuracies or hallucinations to improve response quality over time.

Note: Perplexity does not use a formal fact-checking pipeline akin to journalistic standards, which leaves the door open to inaccuracies or misrepresented data. Because of this, you should always use critical thinking and additional checks to verify information, especially for important decisions.

Perplexity’s Limitations

Despite its strengths and huge potential to revolutionize learning and research, Perplexity has drawbacks and challenges that limit its current uses.

• Potential for Bias: As with any language model, even slight or unconscious leanings in training data can skew generated responses and output tendencies. This has the potential to be even more pronounced when relying more heavily on internet-based sources, where accuracy can’t always be verified (such as Reddit or Quora), for sensitive or controversial topics.
• Accuracy Concerns: Perplexity still sometimes generates incorrect or misleading information, especially around rapidly evolving topics. It can also give more weight to certain perspectives on a topic than they deserve, so users should be vigilant about identifying if certain arguments aren’t supported by the same level of research.
• Niche Topics: If relatively limited web content about a niche topic exists, Perplexity may generate a surface-level answer that doesn’t fully answer the question or hallucinates information to fill in gaps.
• Limited Personalization: Perplexity’s limited memory means that it doesn’t have persistent personalization. This results in preferences of tone, sources, format, etc. not being maintained between sessions, all of which need to be specified in each conversation.
• Multimedia Limitations: Currently, Perplexity can’t summarize video or audio content natively, unless provided with a text transcript. With the popularity of podcasts and YouTube, this can be a major hindrance to the information that it can process and cite.

Perplexity Alternatives

Popular broad substitutes for Perplexity include ChatGPT, Claude, and DeepSeek. These all compete with Perplexity when it comes to the chatbot-based responses to questions, but have drawbacks when it comes to real-time search capabilities. For more research-integrated and search-centric AI tools, Google Gemini and Microsoft Copilot are the most well-known alternatives to Perplexity. All three of these platforms provide real-time web-connected conversational responses with direct source citations.

Smaller or more niche Perplexity alternatives include Elicit AI, Consensus, You.com, Brave Leo AI, and Andi Search.

Learn more about Perplexity’s alternatives for businesses here.

How is Perplexity AI Different from ChatGPT?

One of the most common comparisons between AI tools is ChatGPT vs. Perplexity. While these tools have different goals and methods of generating information, they are often used for the same use cases. Let’s break down some of their differentiating factors:

Use ChatGPT for creative outputs, custom workflows, coding, and writing drafts.

Use Perplexity for factual, current, source-backed research or deep exploration of a subject.

See our in-depth comparison of Perplexity and ChatGPT’s ratings, pros and cons, features, and more here.

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