I built a national parks platform with an AI trip planner that uses both GPT-4 and Claude as providers. Wanted to share what I found running both on the same use case. The planner takes your dates, interests, fitness level, group size, and budget and builds a day-by-day itinerary for any of the 470+ sites in the U.S. National Park System. What I noticed running both models: ∙ GPT-4 tends to give broader, safer recommendations good for first-time visitors ∙ Claude gives more specific and opinionated suggestions better for people who know what they want ∙ Both hallucinate trail names occasionally so I cross-reference against real NPS API data ∙ Chat history is saved so users can revisit and continue past trip plans The AI planner sits on top of real data from 12 NPS API endpoints — so it’s not just generating from training data, it has access to actual activities, campgrounds, alerts, events, and weather for each park. https://www.nationalparksexplorerusa.com/ Curious if anyone else has built tools using dual LLM providers — how do you handle the differences in output style? submitted by /u/peakpirate007
Originally posted by u/peakpirate007 on r/ArtificialInteligence