Ensuring Reliability in GenAI Powered Travel Assistant

Evaluation framework delivers consistent performance and reliability for travel assistant

Our client already had a generative Al-powered travel assistant to assist with bookings, flights and itineraries. They wanted to improve the customer experience by enhancing the reliability and trustworthiness of the travel assistant.

We developed a modular evaluation framework for our client that can be integrated into any generative AI solution. Our framework covers various evaluation metrics, such as reliability, safety and security, relevance, and privacy.

The enhanced travel assistant automates the entire travel planning process, from creating personalized itineraries to conducting real-time internet searches for the availability, fares of flights and hotels. Given the complexities of handling such tasks with a probabilistic model like a Large Language Model, it’s critical that the responses are accurate, relevant, and safe. The evaluation framework was specifically designed to assess these aspects, and to continuously score the quality of the responses generated by the assistant.

In this blog, we’ll dive into the details on how this evaluation framework improved the reliability of its travel assistant with a GenAl-powered solution developed by Infogain.

Intelligent Travel Assistant

Our client’s travel assistant uses automation to simplify the travel journey of key tasks such as:

• Generating detailed and personalized itineraries based on user inputs.
• Identifying and recommending flights that align with the user's preferences and travel constraints.
• Suggesting hotels and facilitating bookings based on real-time availability, user preferences, and reviews.
• Conducting live searches on the internet for current information on flights, hotels, destinations and other travel details, thereby providing up-to-date results to the users.

The primary challenge was to ensure that the responses generated by Al are accurate and trustworthy across various user requests.

4 key metrics for evaluation

Our framework evaluates the travel assistant's responses on four critical dimensions to guarantee high-quality outputs:

Reliability is measured by the accuracy and correctness of the generated responses. The assistant must deliver precise and factual results, such as recommending flights or hotels based on real-time data and user preferences, not based on some static data or pre-trained model.

Safety & Security ensures that responses are free from harmful, unsafe, or inappropriate content. In the context of travel, this includes verifying that recommendations do not pose any safety risks or lead to unethical outcomes.

Relevance is assessed on how well the generated response aligns with the user's query. For instance, if a user searches for hotels within a certain budget, the assistant must provide options that fit in those criteria accurately.

Privacy of the user is safeguarded by ensuring no sensitive information is shared or leaked in the responses. This metric is particularly critical when the solution interacts with real-time internet searches or user-specific travel data.

Seamless integration in production environment

The evaluation framework's architecture is designed to function seamlessly in the background, continuously assessing responses during real-time user interactions. Whether users are creating itineraries or searching for flights and hotels, each response is automatically evaluated across the four key metrics. The system then generates a comprehensive score for every response, providing valuable insights into the trustworthiness of the LLM's outputs.

Figure 1: Architecture of evaluation module in production environment

Sample evaluation results

Below is an example of the response generated by the travel assistant, evaluated across reliability, safety & security, relevance, and privacy. These scores offer clear insights into the quality of the system's outputs, ensuring consistent performance in real-world travel scenarios.

Figure 2: Sample response

Figure 3: Metrics description for Infogain’s GenAI evaluation module

Conclusion

The integration of this evaluation framework ensures that the GenAl-powered travel assistant simplifies travel planning in a reliable, safe, and privacy-conscious manner. By assessing the generated responses across the key metrics, the system continuously refines itself, offering users an experience that they can trust, whether they're searching for flights, booking hotels, or building travel itineraries. The framework ensures that the solution meets the highest standards of accuracy, relevance, and security, making it an indispensable tool for modern travelers.