
What I like most about ChatGPT is how useful it is as an all-in-one AI assistant for both learning and practical work. I regularly use it for brainstorming, coding and debugging, understanding AI/ML concepts, building n8n automations, working with APIs, and designing AI agent and RAG workflows. It has been especially helpful while developing my AI projects because I can explain a problem, share errors or requirements, and iteratively improve the solution instead of starting from scratch. I also use it for research, writing, project planning, and preparing for freelance work and assessments. The ability to maintain context during a conversation makes it much easier to work through complex tasks step by step. Review collected by and hosted on G2.com.
The main limitation is that ChatGPT can sometimes give confident answers that still need to be verified, especially when working on complex coding or AI/ML problems. In long technical conversations, it can also occasionally lose track of earlier requirements or suggest code that needs debugging before it works in my environment. When I integrated OpenAI into an n8n RAG workflow, I found that getting reliable results depended on good prompting, model configuration, retrieval quality, and API setup. I would like stronger consistency and better awareness of when an answer is uncertain, along with easier debugging and troubleshooting for integrations. Review collected by and hosted on G2.com.

