FoundationaLLM vs. Perplexity

Perplexity is helpful when you want auto-code generation for day-to-day problem scenarios such as Powershell script to accomplish a task, Code to invoke a REST API, Class generation from JSON/XML data, etc. It is also helpful when you want to correct or optimize code that you have self-written. Perplexity might not be best suited for scenarios when you need 100% accuracy without your self-verification of correctness.

Incentivized

• Research the company that my prospects work for.
• Research background on my prospects.
• Combine those two and come up with about one page on how to approach them based on the space (persona) I created.

Incentivized

• Image generation is not upto mark
• Data analysis for excel is way off
• Getting insights on data not available online is hard
• Need to embed like copilot in assisting day to day activities

Incentivized

It's great, but doesn't necessarily feel like it adds enough value over using CoPilot, ChatGPT, or whichever generative AI tool your business already uses. Time will tell if this model continues to be developed, and whether it remains competitive with the other big models out in the industry, and innovating features and integrating more use cases fast.

Incentivized

I use all three at present, Perplexity IS outstanding when It comes to researching, like a search engine on steroids. If you need programming skills Claude or Chatgpt seem better suited for the task. What I normally do in a project IS use all three in order to arrive at a more insightful outcome. The BEST of all world.

• Increases efficiency
• Saves costs (e.g. for creation of creative content)
• Frees up time that can be used for other productive actions

Incentivized