Planning multi-city trips by having AI create an itinerary, then systematically refining it through rounds of feedback—adjusting travel times, swapping activities, or changing the order of stops based on what you learn from each proposal. The iterative approach handles the complexity of juggling multiple locations, travel logistics, and overlapping preferences.
Iterative refinement is the process of progressively improving an AI-generated itinerary through a series of follow-up prompts, each adding constraints, corrections, or new preferences to the previous output. Rather than expecting a perfect plan from a single prompt, travelers build toward their ideal trip through structured conversation turns.
This technique matters because complex multi-stop trips involve too many variables to resolve at once, and AI performs significantly better when problems are broken into sequential steps. By refining one leg, budget layer, or activity type at a time, travelers produce itineraries that are far more accurate and personally relevant than those generated in a single exchange.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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