Live AI SEO Tips Using Perplexity
Perplexity is a highly effective AI search engine with a wide range of applications. This video aims to highlight some of its most impressive features, along with demonstrating how I personally utilize this powerful tool.
One of the key methods involves leveraging its capability to perform autonomous internet searches.
You can provide it with a data prompt to locate relevant information.
For example, you might ask, "Can you find real data that could support this article? Don’t add the data to the article, just provide a summary of interesting data points. Include historical data where available for comparison."
Then, observe how it conducts the research—it's fascinating to see it in action. First, it analyzes the question, formulates the query, and then performs several iterations of the search.
This process is inherently agentic, as it doesn’t immediately generate data or perform a search. Instead, it first carefully considers the question, which is a significant feature of AI that usually requires complex programming.
As a result, the responses are generally more aligned with what you’re asking because Perplexity AI Pro incorporates a reasoning step. This reasoning phase is crucial for producing accurate results.
This difference is further evident when comparing the API to the Pro version. If you’re unfamiliar, Perplexity API utilizes Llama 3.1 but delivers significantly less effective results than the Pro version of Perplexity. In fact, I’m considering replacing Perplexity with my own scraping system due to my dissatisfaction with the Perplexity API. This clearly shows that the real strength of Perplexity lies in its frontend, not the API.
The true power of Perplexity comes from its agentic workflows.
These workflows allow AI to emulate reasoning, which can be described as understanding a problem before attempting to solve it.
You can think of Perplexity as a primary agent that formulates questions for a secondary search agent, which then gathers key information from search results.
If you examine the actual search process in Perplexity, it’s surprisingly straightforward.
Read the user's prompt
Generate 2-3 steps to accomplish the task
Generate questions or research specific topics within each task, repeating as needed
Answer each question step by step
Consolidate all information with a final consolidation prompt
This approach, known as Fusion Chain prompting, marks a significant improvement over Perplexity's original concept, which involved conducting basic Google searches, scraping the results, and attempting to answer the question based on those findings.
Fusion Chain prompting greatly enhances accuracy and significantly improves the quality of results, which is why I recommend using Perplexity Pro for research tasks—unless you have a comprehensive system like Harbor that handles all of this for you (as we do).
For this stream we're gonna go through Perplexity Pro and how to master it for your SEO!
You're not gonna wanna miss this one!
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