Winning the Airfare Game Without Outsmarting the Algorithm
WEEK 93 :: POST 1 :: GOOGLE GEMINI
Directions Given To The A.I. This Week+
Instructions Given to each A.I. — Please provide 3 prompt variations that share this objective:
Each A.I. also received two static attachments: the blog post template (structure) and the authoring instructions (voice and standards). The text below is the week-specific assignment as sent — reflowed for the web; wording unchanged.
I'd like you to write this week's Ketelsen.ai post. Two files are attached: the blog post template (the structure to follow) and the authoring instructions (context, voice, and standards). Please read both before you begin, then produce the complete post in a single response.
This week's theme: "Getting the Flights Right" — Airfare Strategy.
This is Week 3 of an eight-week series on planning a vacation with AI. Week 1 established the reader's real constraints — a validated budget ceiling and a constraint profile. Week 2 turned that into a chosen destination, or a short ranked list of finalists. This week they buy the hardest part of the trip.
Flights are where vacation budgets are won and lost, and where most travellers feel least in control. Prices move daily, the rules are opaque, and the internet is full of confident advice that is either outdated or was never true. The job this week is to give the reader a defensible booking decision — knowing what a fair price looks like for their route, when to buy, what to trade, and when to stop optimising and just book.
The three prompts should help a reader work through:
- What "a good price" actually means for their specific route — a fare is only cheap relative to that route's own history and season, and a reader with no baseline cannot tell a deal from a markup.
- Timing and the cost of waiting — how to decide whether to book now or hold, and how to put a number on the risk of waiting rather than guessing.
- The real trade space — connections, nearby airports, off-day departures, red-eyes, basic-economy restrictions, and baggage. Each saves money and spends something else; the reader should see the exchange rate, not just the headline fare.
- Total cost, not ticket price — seats, bags, changes, and the ground transport a cheaper outlying airport quietly adds back.
- When to stop — a stopping rule that prevents weeks of fare-watching for a saving that no longer justifies the attention.
The output a reader should walk away with is a booking decision they can defend: this fare, on this routing, bought now or held until a stated date, for these reasons.
A note on the strongest version of this week: at the advanced end, this is a fare decision framework — a baseline for the route, a target price, a walk-away price, a hold-or-book rule tied to a date, and an explicit list of the trade-offs the reader will and will not accept. That structure is worth reaching for.
A hard constraint, and the most important instruction in this brief. AI models cannot see live fares, and their price knowledge is stale by construction. No prompt in this post may ask the AI to state a current price, predict a specific future fare, or claim what a route "usually costs" right now. That is the single most damaging thing an AI can do to a traveller in this domain — it produces confident, checkable, wrong numbers, and the reader finds out at the checkout page.
Design the prompts so the AI does what it is genuinely good at: structuring the decision, naming the variables, building the comparison framework, and telling the reader what to go and look up. The reader supplies the live data from a fare search; the AI turns it into a decision. Prompts that make this division of labour explicit are the strongest possible answer to this week's theme, and posts that blur it should expect to be marked down on Practical Utility.
Series dependency chain, for the Metadata block: Week 3 consumes the destination (or final shortlist) chosen in Week 2 and the budget ceiling validated in Week 1 — the airfare decision is scored against both, and a fare that breaks the ceiling is a signal to revisit the destination, not to quietly raise the budget. Week 3 produces the confirmed routing and dates, which Week 4 (lodging) and Week 5 (itinerary) both assume. Locked flights are what turn a plan into a trip.
Because readers may arrive at this post without having read Weeks 1 and 2, the prompts should work for someone who knows roughly where they are going and what they can spend, while making clear they get far more from them with a real constraint profile and a chosen destination in hand.
Three difficulty tiers as always — Beginner, Intermediate, Advanced — each a genuinely different approach to the same problem, not the same prompt at three lengths.
On examples: this is a consumer travel topic. The template lists tech startup / retail / freelance as suggested industry examples — those are marked MAY, and this week you should almost certainly adapt them. Families coordinating school holidays, couples with mismatched leave, solo travellers with flexible dates, and people flying to a fixed-date event are the right contexts here. Choosing them over the suggested business examples is correct behaviour and will not be scored against you.
A note on supplied figures. Anything marked `[SUPPLIED — use as given]` above came from Ketelsen.ai's own research brief. Use it freely — you are not fabricating by repeating it, and you will not be marked down for leaving it uncited. Do not attach an invented source to it. (No supplied figures this week. Given the live-pricing constraint above, this week is a particularly bad one to invent any — if you find yourself reaching for a number, that is the signal to restructure the prompt so the reader supplies it instead.)
## BEFORE YOU SUBMIT — STRUCTURAL CHECK
(This block is identical every week. It exists because these specific items are the ones posts drop, and a dropped structural item costs compliance points for something that takes one minute to add.)
Your post is parsed by a script before any human reads it. Confirm all seven:
1. ☐ Response begins with `PLATFORM: <your name>` and `WEEK: 3` 2. ☐ `## Lead` present once, at the very top, before Variation 1 3. ☐ `## In one line` present in all three variations 4. ☐ `## What this prompt gives you` present in all three variations 5. ☐ `## The Prompt` present in all three variations, with the prompt in double quotes beneath it 6. ☐ `## Introductory Hook` and `## Current Use` present in all three variations (three of each — not one) 7. ☐ Every template heading written as `##`, none bolded instead; prompt breakdown is running text split on ` : `, with no `###` headings inside it
A complete post has 57 `##` headings. If your count is well short, a section is missing or was bolded instead of hashed.
One extra check this week: confirm no prompt asks the AI to state, predict, or recall a specific airfare. If one does, restructure it so the reader brings the fare and the AI brings the framework.
Airfare is where vacation budgets are won or lost, and it is the single purchase that makes travelers feel the least in control. Prices fluctuate daily, rules are intentionally opaque, and attempting to outsmart airline algorithms usually leads to decision paralysis. Because AI models cannot see live prices and their training data is stale, the worst thing you can do is ask one to predict a fare. Instead, these three prompts use AI for what it does best: structuring complex trade-offs. From a quick hidden-fee sanity check to an advanced routing matrix, these prompts turn the live flight data you supply into a bulletproof, defensible booking decision.
The Flight Sanity Checker
Expose hidden airline fees and pick the truly cheapest flight option.
When you search for flights, the price in big bold text is rarely what you actually pay. Basic economy fares strip away carry-on bags, seat selection, and flexibility, masking the true cost of the journey until you reach the checkout page. The Flight Sanity Checker takes the raw options you found and forces you to account for the hidden extras. It breaks the illusion of the headline price, ensuring you compare apples to apples before you hand over your credit card.
Why this matters now
Airlines are unbundling their services faster than ever, making standard fare comparison tools less reliable for bottom-line budgeting. Right now, this prompt is essential for cutting through the marketing noise of "deal" prices. It gives you a structured way to evaluate the true financial impact of an itinerary today, so you don't end up paying premium prices for a budget ticket at the gate.
Act as a practical travel-planning assistant. I am booking a flight to \[Destination\] and have a maximum budget of \[Budget\]. I have found these three current flight options: \[Paste flight options, airlines, prices, and layovers\]. Do not guess or predict any prices yourself. Based on the airlines and routings provided, list the specific hidden costs I need to look up for each option (e.g., carry-on fees, seat selection, transport from outlying airports). Once I reply with those costs, calculate the true total for each flight and recommend the best option based on my budget.
How the AI reads this prompt
Practical examples from different industries
The Family Holiday:
A family of four is flying to Orlando. They input a $200 Frontier flight and a $350 Delta flight. The AI tells them to look up bag fees and seat selection (crucial for sitting with kids). Once supplied, the AI reveals the "cheap" Frontier flight actually costs $20 more per person when bags and family seating are factored in. The Solo Backpacker: A backpacker traveling to London inputs a cheap Ryanair flight to Stansted and a slightly pricier British Airways flight to Heathrow. The AI prompts them to look up the train costs into central London from both airports. The true cost calculation proves the Heathrow flight saves both money and three hours of transit time. The Weekend Wedding Guest: A couple flying to a wedding inputs a basic economy fare and a standard main cabin fare on United. The AI prompts them to check carry-on allowances. Since they are only going for two days, they realize they can share one checked bag on the basic fare, keeping them under budget while still getting their formal wear there safely.
Creative use case ideas
> 1. Award Travel Optimization: Input different points-booking options and have the AI tell you what cash surcharges or taxes to look up, revealing the true "cents per point" value. > 2. Road Trip vs. Flying: Input gas/hotel estimates for driving versus flight costs, having the AI prompt you for wear-and-tear or airport parking fees. > 3. Sports Equipment Transit: (Non-business) A traveling amateur hockey team inputs their flight options, and the AI guides them to look up oversized baggage fees, which wildly skew standard ticket prices. > 4. Pet Travel: Input flight options and ask the AI what in-cabin pet fees or specific airline blackout dates to research before committing.
Adaptability tips
You can easily adapt this prompt for complex ground transit (like Japanese rail passes versus domestic flights) by swapping out "airlines" for "transport methods." If you have strict time constraints, add "Ask me to value my time per hour" so the AI factors the cost of a six-hour layover into the final calculation.
Pro tips
> 1. Specify the exact airline fare class (e.g., "United Basic Economy" vs "United Economy") in your input; the AI knows the general structural differences between these tiers and will give much sharper follow-up questions. > 2. Tell the AI if you hold a specific travel credit card or airline status, as it will automatically cross-reference fees you might be exempt from (like free checked bags).
Prerequisites
You need to have completed a live search on Google Flights, Skyscanner, or an airline website and have 2-3 specific routings and their base prices ready to paste into the prompt.
Required tools
Any general-purpose AI (ChatGPT, Claude, Gemini). Free tiers are perfectly adequate.
Frequently asked questions
Why can't the AI just look up the baggage fees for me?
AI models operate on training data that is cut off at a certain date, and airlines change their fee structures constantly. If the AI guesses, it might give you a fee from 2023\. Supplying the live data yourself guarantees your budget is accurate. What if all the options end up over my budget? The AI will calculate the math exactly as provided. If everything exceeds your budget, that is a clear signal to return to Week 1's budget ceiling or Week 2's destination choices to adjust your baseline expectations. Does this work for international flights? Yes, and it is arguably more important there. International flights often have complex rules around budget carriers, airport taxes, and visas. The AI will prompt you to look up entry fees or specific international terminal transit costs.
Recommended follow-up prompts
> 1. The Lodging Location Matrix: (Coming in Week 4\) Now that you know your arrival airport and true flight cost, evaluate where to stay based on transit times. > 2. The Layover Maximizer: If your cheapest option involves a 9-hour layover, prompt the AI to build a mini-itinerary for that specific transit city.
Tags and categories
Tags:
flights, budgeting, hidden fees, travel planning, cost comparison Categories: Personal Finance, Travel Logistics
Citations
NOT APPLICABLE
The Book-or-Wait Matrix
Define your walk-away price and set a firm deadline to book.
One of the most stressful parts of vacation planning is watching a flight price fluctuate and wondering, "Will it drop on Tuesday?" Spoiler: the old rule about buying on Tuesdays is a myth. The longer you wait for a magical price drop, the more you risk the fare skyrocketing out of reach. The Book-or-Wait Matrix shifts you from emotional guessing to objective strategy. It establishes clear price targets and a firm deadline, so you can stop checking fares every six hours and book with confidence.
Why this matters now
Dynamic pricing algorithms mean fares can change multiple times a day based on route demand, browser cookies, and inventory. For travelers planning trips right now, decision fatigue sets in quickly. This prompt cuts through the anxiety by establishing a stopping rule—a predefined set of conditions under which you will simply buy the ticket and never look back.
Act as an objective travel pricing strategist. I am flying from \[Origin\] to \[Destination\] on \[Dates\]. My hard budget ceiling is \[Budget\]. The current best price I can find is \[Current Price\]. I need to decide whether to book now or hold. Do not predict future prices or tell me what this route usually costs. Instead, ask me to look up the historical price range for this route on Google Flights. Once I provide that range, help me establish three things: a 'Target Price' (a great deal I should buy instantly), a 'Walk-Away Price' (my absolute limit), and a 'Drop-Dead Booking Date' (when to stop waiting). Ask me for my risk tolerance (low, medium, high) to help calculate this.
How the AI reads this prompt
Practical examples from different industries
The Holiday Traveler:
A student flying home for Thanksgiving has a $400 budget and sees a $350 flight. They tell the AI they have "zero risk tolerance" for missing the trip. The AI sets the Target Price at $350, the Walk-Away at $400, and advises a Drop-Dead date of today, explaining that holiday flights rarely dip and the peace of mind is worth securing now. The Flexible Remote Worker: A freelancer wants to work from Lisbon for a month. They see a $700 fare but have a "high risk tolerance." They supply a historical range of $500-$900. The AI sets a Target of $550, a Walk-Away of $800, and a Drop-Dead date of 21 days before departure, maximizing the window for a price drop while capping the downside. The Concert Goer: A group of friends is flying to Chicago for a specific concert weekend. The current fare is $280, budget is $350. They input a "medium risk tolerance." The AI sets a target of $250, a walk-away of $325, and a Drop-Dead date of 6 weeks prior, noting that event weekends sell out suddenly, making waiting highly risky.
Creative use case ideas
> 1. Cruise Repositioning Flights: Use it to decide when to book one-way open-jaw flights for a cruise, where dates are strictly inflexible but routes are unusual. > 2. Last-Minute Event Travel: (Non-business) A family needing to fly for an unexpected life event can use this with a "24-hour drop-dead date" to quickly establish if they are being price-gouged. > 3. Conference Travel: Independent consultants paying their own way can define exactly when a flight becomes too expensive to justify attending the networking event. > 4. Tracking Points Redemptions: Swap "price" for "miles" to decide whether to transfer credit card points now or wait for a transfer bonus.
Adaptability tips
If your dates are flexible, add "My dates can shift by \+/- 3 days" to the prompt. The AI will then ask you to check the flexible date grid and adjust your Target Price lower, factoring in your willingness to fly on a Tuesday or Wednesday.
Pro tips
> 1. Use Google Flights' "Price Graph" feature to get the historical data the AI asks for; it is the most reliable consumer-facing tool for this specific metric. > 2. Tell the AI your destination's high/low season. A price that looks terrible in November might be a historic low for peak July, and the AI can contextualize your target price based on seasonality.
Prerequisites
You must know your route and dates, have a validated budget ceiling from Week 1, and have a browser tab open to Google Flights so you can quickly retrieve the historical price range when the AI asks for it.
Required tools
Any general-purpose AI platform. Google Flights (or a similar tracker like Hopper) is required alongside the AI to provide the historical baseline data.
Frequently asked questions
What is a "Drop-Dead Booking Date"?
It is the exact calendar day you commit to buying the ticket, regardless of whether the price has dropped to your target. It prevents you from waiting so long that prices surge due to last-minute business travelers booking up the remaining seats. What if the price hits my Walk-Away limit before my Drop-Dead date? The framework dictates you either buy immediately before it gets worse, or you walk away from the trip entirely (or shift to a different destination from your Week 2 shortlist). The matrix is designed to protect your budget ceiling. Why does risk tolerance matter? If you must be on a specific flight (e.g., for a wedding), you have low risk tolerance and should buy earlier. If you are just browsing for a weekend getaway and don't care if you go or not, your high risk tolerance allows you to wait much longer for a deal.
Recommended follow-up prompts
> 1. The Destination Pivot: If your flight hits the walk-away price, prompt the AI to retrieve your runner-up destinations from Week 2 and re-run the matrix. > 2. The Seat Map Analyzer: Once you hit your Drop-Dead date, prompt the AI with the current available seat map to determine if paying for a seat assignment is mathematically necessary.
Tags and categories
Tags:
timing, risk management, booking strategy, airfare, decision framework Categories: Strategic Planning, Travel Logistics
Citations
NOT APPLICABLE
The Complete Fare Decision Framework
Map every trade-off to build a defensible, constraint-based booking strategy.
For the advanced traveler, the cheapest flight is rarely the best flight. A $100 saving is instantly erased if it requires a 4:00 AM departure, a six-hour layover, or an airport transfer that costs $120 in taxis. The Complete Fare Decision Framework treats your travel as an equation where time, comfort, and money are all currencies. By combining your Week 1 budget constraints with your Week 2 destination choices and live search data, this prompt builds a rigorous, multi-variable matrix. It forces you to explicitly value your trade-offs, yielding a booking decision that is mathematically and practically sound.
Why this matters now
Travelers frequently fall into the trap of "optimizing" a trip to death, spending twenty hours of research to save thirty dollars. Right now, as airlines introduce increasingly fragmented routing options and dynamic pricing, this prompt cuts the cognitive load. It gives you a strict operational framework to evaluate complex itineraries—like flying into a secondary airport versus a primary hub—and tells you exactly when to pull the trigger.
Act as an analytical travel strategist. I have selected \[Destination\] (from Week 2\) and have a validated flight budget of \[Budget\] (from Week 1). My constraint profile is: \[List constraints, e.g., no red-eyes, max 1 layover, must have checked bag\]. I am currently evaluating these specific routings/airports: \[List options\].
Do not quote live fares or predict prices.
First, provide me with a list of the 5 specific live data points I need to pull from my current search (e.g., base fares, baggage fees, ground transport costs, layover lengths).
Second, once I provide that data, build a multi-variable trade space matrix evaluating cost vs. convenience for each option.
Finally, based on this matrix and my constraints, output a firm go/no-go booking rule, an explicit list of the trade-offs I must accept for the winning option, and a strict stopping rule to prevent further searching.
How the AI reads this prompt
Practical examples from different industries
The Multi-Generational Family Trip:
A family flying to Costa Rica has constraints: "No layovers over 3 hours, grandma cannot walk long distances." They evaluate flying into San Jose vs. Liberia. The AI asks for the live fares, plus the ground transport costs to their resort. The matrix proves that while San Jose flights are $800 cheaper total, the $400 private shuttle and 4-hour drive violate their comfort constraint, leading to a firm rule to book the Liberia routing. The Digital Nomad: A remote worker with flexible constraints ("must have 2 checked bags, don't care about layovers") is flying to Tokyo. They evaluate Zipair vs. ANA. The AI asks for the baggage fees and meal costs on the budget carrier. The matrix reveals that once the nomad's heavy luggage and required onboard amenities are added, the premium ANA flight is actually $50 cheaper, generating an immediate "go" decision. The Marathon Runner: (Non-business) An athlete flying to the Berlin Marathon has strict constraints: "Must arrive 2 days early, cannot risk lost checked baggage." The AI prompts them to research direct vs. connecting flights and carry-on allowances. The framework identifies that paying a $200 premium for a direct flight is the only way to mathematically guarantee their constraints are met, providing a defensible reason to break their initial budget by a small margin.
Creative use case ideas
> 1. Split-City Itineraries: Evaluate the cost of flying into London and out of Paris versus round-trip to London plus a train ticket. > 2. Miles vs. Cash Break-Even: Use the matrix to determine the exact point where it makes sense to drain your frequent flyer account versus paying cash out of pocket. > 3. Medical Travel: (Non-business) Patients traveling for specialized care can input strict accessibility constraints to evaluate the physical toll of layovers against flight costs. > 4. Ski Trips: Evaluate regional mountain airports versus major hubs, factoring in the cost of 4x4 rental cars and oversized sporting equipment fees.
Adaptability tips
If you are traveling with a group paying separately, add "Provide the matrix broken down by per-person cost and per-person trade-offs." This gives you a clean document you can copy-paste to the group chat to justify why you chose a specific itinerary for everyone.
Pro tips
> 1. Assign a monetary value to your time in the prompt (e.g., "Value my travel time at $30/hour"). The AI will mathematically calculate the "cost" of a layover and integrate it into the final trade space matrix. > 2. Ask the AI to output the matrix in Markdown table format for easier reading and comparison.
Prerequisites
You must have completed Week 1 (Budget and Constraints) and Week 2 (Destination Selection). You also need to have conducted an initial live search to know which general routings or airports are actually available.
Required tools
An advanced AI model (Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro) is highly recommended for this variation to ensure the logic in the multi-variable matrix holds up accurately.
Frequently asked questions
Why do I need a "stopping rule"?
Because the internet offers infinite information. You could spend 14 straight days checking fares to save $15. A stopping rule acknowledges that your time has value and prevents analysis paralysis by defining exactly when the search is "good enough." What if the matrix proves my budget constraint is impossible? This is the value of the framework. If the matrix shows that meeting your required constraints costs more than your Week 1 budget, you have a defensible reason to either raise the budget (if possible) or return to Week 2 and pick a more affordable destination. Can I use this for things other than flights? Absolutely. The structure of this prompt—defining constraints, requesting missing data points, and building a cost/convenience matrix—is one of the most powerful ways to evaluate any complex purchase, from booking rental cars to buying software for a business.
Recommended follow-up prompts
> 1. The Itinerary Anchor: (Coming in Week 5\) Take the confirmed arrival times from your winning flight and prompt the AI to build the first 24 hours of your itinerary around them. > 2. The "Basic Economy" Survival Guide: If the matrix dictates you must fly basic economy to hit your budget, prompt the AI to build a packing and logistics plan to survive the airline's restrictions.
Tags and categories
Tags:
advanced framework, trade-offs, constraints, cost-benefit analysis, total cost Categories: Strategic Planning, Advanced Prompting
Citations
NOT APPLICABLE
Which of the three should you use?
The Flight Sanity Checker (Beginner) is designed for immediate clarity. It is perfect for travelers who have already found a few flights and just want to make sure they aren't falling for hidden fees. It requires no prior setup and simply acts as a safeguard against deceptive pricing. The Book-or-Wait Matrix (Intermediate) tackles the psychological aspect of booking. It is for the traveler staring at a price, wondering if they should pull the trigger. By introducing concepts like historical baselines and risk tolerance, it shifts the user from guessing to executing a predefined strategy. The Complete Fare Decision Framework (Advanced) is a comprehensive strategic tool. It consumes the foundational work done in Weeks 1 and 2 and turns it into a rigorous mathematical matrix. While the beginner prompt asks "which is cheaper?" and the intermediate asks "when do I buy?", the advanced prompt forces the traveler to evaluate the total exchange rate of their money, time, and comfort.
Gemini's post shipped with escaped markdown in its reader prompts. The post as delivered contains 32 backslash-escaped characters, 25 of them inside the prompts you are meant to copy — so the placeholders read \[Destination\], \[Budget\] and \[Origin\] instead of [Destination], [Budget] and [Origin].
Paste one of those prompts into a chatbot and the backslashes go with it. They are harmless — every model will read straight through them — but they are not what Gemini meant to write.
The judge did not mention this and it did not affect the scoring. Gemini placed third this week on citation quality, having sourced nothing at all; the stray backslashes had no part in that.
We have not corrected the post. Ketelsen.ai is an experiment in what these models actually produce from an identical brief, so what they produce is the finding — including the untidy parts. Editing it, or telling the models in advance not to do it, would quietly delete the observation.
Worth noting, since this is the third week running: Gemini has emitted escaped markdown in every week of this series so far, and Claude has emitted none. That is a real difference between the two, and it is the kind of thing this series exists to surface.
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