Most people still treat AI search engines like glorified encyclopedias, but they are missing the bigger picture. I recently came across a post by an AI expert that completely reframed how I look at Perplexity. This creator suggests that the new “Labs” feature isn’t just an upgrade; it’s practically a dedicated project manager living in your browser.
The post’s author explains that Labs operates differently than standard search. Instead of just fetching a quick answer, it enters a “deep work” mode. It combines browsing live data, writing code, generating charts, and formatting images to build comprehensive outputs. Think of it less like a chat and more like assigning a complex task to a team member who goes away for a bit and comes back with a polished slide deck or report.
📌 From Prompt to Full Strategy
The original poster highlights the ability to generate entire Go-To-Market strategies with a single request. By toggling the bulb icon, you aren’t just getting text; you are getting a structured dashboard. The expert notes that because Labs can run code, it can visualize data instantly, meaning you can ask for a market analysis and get back actual graphs and code-backed data visualizations rather than just paragraphs of text.
💡 The “Set and Forget” Workflow
This LinkedIn user outlines a specific workflow that turns Perplexity into a productivity engine. The process is simple: click the bulb icon to enable Labs, input a detailed prompt with constraints, and let it run. Since it takes longer than a standard search, the author suggests treating it like an asynchronous task. You assign the work, go grab a coffee, and return to a “richer output” that handles the multi-step reasoning you usually have to do manually.
✅ Optimization Techniques
To get the most out of this tool, the creator emphasizes the need for clarity. You can’t just throw vague ideas at it. The expert advises adding specific context and constraints to your prompts to avoid generic results. They also stress the importance of iterative tweaking. Don’t expect perfection on the first try; instead, use the follow-up feature to refine the report, just as you would give feedback to a junior employee.
However, the original poster is honest about the limitations. Because this feature is doing heavy lifting with code and design, it is significantly slower than a simple query. The author also warns that it can still “hallucinate” data or visuals, so blind trust is dangerous. Plus, transparency is limited regarding the specific steps it takes to reach a conclusion, so you always need to verify the output manually!
I’m definitely going to test this out for my next weekly report. You should check out the full post to see the specific examples the author shared.