Black Friday Deal Research Prompt Generator
Build a properly specific Black Friday/Cyber Monday shopping research prompt — budget, priorities, and comparisons included, for ChatGPT, Perplexity, or Gemini.
AI models are good at comparison and research when given real constraints -- budget, priorities, specific options -- and much weaker at guessing what you want from a vague, open-ended question.
How to use it
Be specific about the product or category, and give at least a rough budget -- this is what turns a vague question into an answerable one.
Naming two or three real products gets you an actual comparison, not just a generic recommendation.
Copy the prompt into ChatGPT, Perplexity, or Gemini -- a tool with live search access will give more accurate, current pricing information.
Tips for better results
- Always include at least a rough budget Without one, the AI has to guess a price range, which usually means a generic, unhelpful middle-of-the-road answer.
- Ask specifically whether a discount is real Retailers sometimes inflate a "was" price before discounting -- the thorough research depth option asks the model to flag this.
- Compare two or three specific products, not a broad category "Compare X vs Y" produces a far more useful answer than "what are the best options," which tends to return a generic list.
Example output
“I'm shopping for a pair of noise-cancelling headphones during Black Friday/Cyber Monday. My budget is under $250. My top priority is the best overall value for the price, not necessarily the cheapest option. Specifically compare: Sony WH-1000XM5 vs Bose QuietComfort Ultra. Must-have features: good for travel, at least 20 hour battery life. Compare the top 3-4 options in a short table -- price, key specs, and the main tradeoff for each. Additional context: it's for daily commuting and occasional flights.”
TL;DR
This year, a large share of Black Friday and Cyber Monday shoppers are asking ChatGPT, Perplexity, or Gemini to help find and compare deals directly, rather than digging through deal sites themselves. The problem is that most people ask a vague question — “what’s a good Black Friday deal on headphones” — and get back a generic, unhelpful list.
Why a vague shopping question gets a useless answer
AI models are good at comparison and research when given real constraints to work with — budget, priorities, specific options — and much weaker at guessing what you actually want from an open-ended question. “What’s a good deal on a TV” has no useful answer, because “good” depends entirely on your budget, room size, and what you’ll use it for. This generator front-loads exactly the details that turn a vague question into a genuinely answerable research task.
What each field contributes
- What you’re shopping for is the only required field — the product or category itself.
- Budget narrows the entire response — without it, the AI has to guess at a price range, which usually means a generic, unhelpful middle-of-the-road answer.
- Top priority tells the AI what to actually optimize for: lowest genuine price, best overall value, specific features, or fast reliable delivery — four different answers to what sounds like the same question.
- Specific options to compare is where this becomes genuinely useful research rather than a generic recommendation — naming two or three real products gets you an actual comparison, not just a suggestion.
- Must-have features filters out options that don’t fit your actual situation, rather than getting a generic “best overall” pick that ignores your specific needs.
- How deep should the research go controls the format — a quick single recommendation, a comparison table, or a thorough breakdown that checks whether a “deal” price is actually a meaningful discount.
- Extra context is where you add anything that changes the calculus — it’s a gift, you already own a competing product, timing constraints.
Getting the most useful research out of this
Timing matters more for this than most prompts on this site — pricing and deal information change by the day, sometimes by the hour, during Black Friday week. If your AI tool has live search access, mention that you want current pricing specifically, since some models default to training-data knowledge that can be months out of date for a fast-moving pricing question. Running the same prompt again a day or two later, closer to when you actually plan to buy, often surfaces different information than an earlier check.
For anything over roughly $100, it’s worth asking the follow-up “has this price actually dropped compared to the last few months, or just compared to its original list price” — inflated “was” prices are common enough during Black Friday that this one follow-up question catches a meaningful number of deals that aren’t actually deals.
FAQ
Which AI tools does this prompt work with?
Any AI assistant that can search or has current information, including ChatGPT, Perplexity, and Gemini — since this involves current pricing, a model with live search access will generally give more accurate, up-to-date answers than one without it.
Can this check if a deal is actually a good price historically?
The “thorough, with price-history check” depth option asks the model to flag this specifically — results depend on the model actually having or finding that pricing context, so cross-checking with a price-history tool for anything expensive is still worth doing.
Do I need to know exactly what I want before using this?
No — leave “Specific options to compare” blank and the prompt will ask for a recommendation instead of a comparison, which works well if you’re still deciding what to buy at all.
Can I use this for researching a gift for someone else?
Yes — mention that in the Extra context field along with anything relevant about the recipient, and the research will account for it.
Does this work for Small Business Saturday too, not just Black Friday and Cyber Monday?
The prompt itself works for any shopping research, but see the Small Business Saturday prompt linked above for one specifically built around finding and supporting small, independent sellers.