- AI-Powered Travel Planner

Prompt Engineering
MVP of AI-based product
Chat GPT
Google Places
Case Summary is an AI-powered travel planning application that streamlines the process of creating customizable trips. Users select the number of days, country, and preferences, and generates a detailed plan and route based on their inputs.
User-directed trip planning helps travelers to shape their own journey, selecting destinations and determining trip duration.

This information alone enables the application to generate personalized trip plan.
Trip planning in no time
Tailored tour plan
Moments later, the AI engine generates the most interesting attractions in the selected travel direction.

The plan is logically tailored to match the user's preferences (whether they prefer a lazy or active trip).
Personalized itinerary
Customizable destination
The suggested trip destinations can be adjusted. Users can choose a different itinerary for a specific day, or even alter a single destination.

This feature avoids repeating previously visited or uninteresting locations in trip planning.
Flexible Tour Adjustments
Destination insights
Travelers seeking depth in their journey can rely on to offer historical and entertaining details.

If a meal break is needed, the app conveniently suggests nearby restaurants and cafes.
Historical and fun facts now!
To ensure travelers won't miss any captivating sights, “ Now” provides real-time access to the area's top attractions.
Live Guide
Technical Explanation of AI Features leverages the ChatGPT API to provide suggestions. It asks the user for basic requirements and incorporates the answers into a specific tailor-made prompt. For example, when a user selects "one" city for their trip, adds a sentence to the prompt, such as I want to visit only one city during the trip.
Such a dynamically constructed prompt is then sent to OpenAI's GPT API. A crucial part of the solution is that the prompt requests a response in a well-defined JSON format, which greatly simplifies the application, as it eliminates the need for additional processing.
The generated tour description is dependent on input values and user preferences. Users can view proposed destinations, descriptions, ratings, and photos. The "fun facts" feature enables further interaction with AI, allowing users to learn new and interesting details about attractions. Users can also use Google Maps to navigate directly to a specific point of interest.
If a user dislikes a suggested attraction, they can change it by swiping left. This prompts another ChatGPT API request that provides a new alternative based on user preferences. Duplicate suggestions are avoided by creating the underlying prompt accordingly.
After finalizing the tour plan, users can save it to their personal database with a unique name for future reference.
Main Challenges
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One of the challenges involved designing and refining the dynamic prompts to enable accurate and satisfying responses. The prompts evolved considerably over the development period, ensuring logical geographical planning and viable travel suggestions.
Adapting AI Solutions
Leveraging the OpenAI GPT to return answers in a JSON format was a novel approach to speed up the design and development process, while also mitigating the need for writing algorithms for data transformation.
Next steps
To enhance's performance and functionality, we have identified possible short-term roadmap items:
Photo Recognition
By scanning users' vacation photos, we will be able to provide better trip suggestions based on their travel history and activities.
API Integrations
We plan to link with Google Calendar for tour scheduling. Integrations with Skyscanner, Airbnb, and will further facilitate seamless travel and accommodation planning.
Speech Recognition
This feature will transform the app into your personal travel agent and guide, reducing the time spent in front of your device screen.
Much like how creates personalized travel plans, AI potential could be used to provide customized health and wellness recommendations gathering relevant information about a user's health habits, and preferences.
An application can use personalized learning plans for students that would adapt to the students' level, and preferred learning style.
Career Planning
A platform could be created to prepare personalized career development plans. Given the user's career preferences, skills, and aspirations, the AI can suggest suitable job paths, professional development opportunities, and mentorship programs.
By interpreting user's style preferences and wardrobe needs, the AI could suggest outfits, coordinate styles, and recommend fashion brands, enhancing shopping and dressing experiences.
Interested to implement a similar AI-driven solution?
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