I organise my prompt library around five categories of work. These are the categories that cover the vast majority of what a travel advisor uses AI for on a weekly basis. Your library may eventually have more categories, or you may subdivide these, but this is the structure that works as a starting point.
Category one: client communications
This is where Module 5 lives in library form. Your frameworks for pre-departure emails, enquiry responses, booking confirmations, post-trip follow-ups, and any other email type that recurs in your practice. Each framework captures the structure that works for that communication type, the client detail you need to include, and the constraints that keep the output on register.
Your Module 5 companion PDF already contains starter frameworks for these. The task in this module is to take the ones you have used, refine them based on the output they produced, and save them as tested library entries rather than course exercises.
Category two: itinerary content
Itinerary introductions, day-by-day descriptive text, destination summaries within a proposal, and the framing language that turns a list of bookings into a narrative your client wants to read. The frameworks in this category handle the structure and tone; you provide the client brief, the itinerary details, and the emotional register for each trip.
A strong itinerary introduction framework, for example, has placeholders for the client’s names, the original brief, one or two specific itinerary details that reflect their priorities, and the emotional note you want the opening to land. The framework ensures consistency. The details you fill in ensure specificity.
Category three: research and analysis
Destination overviews, supplier brief summarisation, option comparisons, client questionnaire synthesis. This is where Module 6 lives in library form. Your research frameworks specify what perspective to research from, what aspects to cover, and what to flag for verification. They are the questions you ask repeatedly, saved so you do not have to compose them from scratch each time.
For advisors working across Southern Africa, research frameworks are particularly valuable because the range of destinations, operators, and seasonal variables is large. A framework for comparing safari lodges against client criteria, or for producing a seasonal briefing on a specific region, saves significant composition time when the research need recurs.
Category four: problem-solving and supplier management
Rebooking requests, flight disruptions, cancellation queries, supplier policy interpretation, and the time-sensitive communications that land on your desk when something changes or goes wrong. This is the category most advisors do not think of as an AI use case, but it is one of the areas where a tested framework saves the most pressure in the moment.
A framework for handling a flight cancellation, for example, has placeholders for the client\u2019s name, the disrupted routing, the alternative options you have identified, what the airline\u2019s rebooking policy covers, and the tone you want to strike: reassuring, practical, and in control. When a client calls at seven in the morning because their flight has been cancelled, you do not want to be composing that email from scratch while simultaneously working the rebooking. You want a framework that lets you fill in the specifics and send something clear and professional within minutes.
Similarly, a framework for interpreting supplier terms and conditions, cancellation policies, amendment fees, what is and is not covered, allows you to upload the relevant policy document and ask the tool to extract the specific clause, summarise it in plain language, and draft a client-facing explanation. The time this saves on a complex cancellation query or a disputed charge is significant, and the quality of the response is higher because you are working from the actual policy language rather than from memory.
Problem-solving frameworks benefit from being built before you need them. The moment a crisis arrives is not the moment to design the framework. Build them during a quiet week, test them on a real scenario, and have them ready for the week they matter.
Category five: business operations
Supplier communications, internal briefing notes, meeting preparation summaries, partnership proposals, and the operational writing that supports your business but does not go directly to clients. This category is often overlooked in AI training because the focus tends to be on client-facing output, but the time saved on operational writing is significant and the quality bar is different: these outputs need to be clear and accurate, but they do not need the same level of voice calibration as client communications.
A framework for preparing a supplier meeting briefing, for example, might ask the tool to summarise the key points from a supplier’s latest update, note any changes from the previous version, and identify three questions to raise in the meeting. Simple, but it saves twenty minutes of preparation every time.
