Google’s AI Mode Started ‘Filling In the Blanks’—Here’s How to Ask Questions So It Doesn’t Invent the Wrong One (and Cost You Real Money)
AI Mode’s biggest risk isn’t obvious hallucinations—it’s quiet assumptions about your budget, location, timeframe, and dealbreakers. Remove the blanks, force it to show its assumptions, and you’ll stop comparing the wrong products, trips, and policies.
By TheMurrow Editorial
April 26, 2026
Key Points
- 1Understand why Google AI Mode “fills in the blanks”: query fan-out and context carryover can lock in the wrong interpretation fast.
- 2Prevent costly drift by stating five constraints upfront—location, budget definition, timeframe, must-haves, and dealbreakers—before asking for “best.”
- 3Force accuracy by making AI Mode show assumptions, cite sources per claim, and compare bounded options with clear trade-offs.
You type a straightforward question into Google and get back an answer you never asked for.
Not a wild hallucination. Not a cartoonish error. Something subtler: a confident, well-written response that quietly assumes your budget, your location, your timeframe, your device, your definition of “best,” and the one constraint that actually matters to you. By the time you notice, you’re already comparing the wrong products, planning the wrong trip, or reading the wrong policy.
Google calls its new experience AI Mode “the most powerful AI search.” It’s designed for complex, multi-part questions, with follow-ups, and it includes links to the web. It’s also, by Google’s own admission, early-stage and “won’t always get it right.” That combination—high authority plus occasional wrongness—creates a new kind of user risk: not that AI will babble nonsense, but that it will “fill in the blanks” in ways that cost you time, trust, and sometimes money.
The danger isn’t that AI Mode can’t answer your question. It’s that it can answer a better-written version of a different one.
— — TheMurrow
What Google’s AI Mode is—and what it isn’t
Google introduced AI Mode in March 2025 as an experiment inside Search, initially tested through Labs, and expanded its visibility in the U.S. around May 2025, during the Google I/O timeframe. Google describes AI Mode as a conversational, AI-generated search experience aimed at “more advanced reasoning and multimodality,” where you can ask complex questions and then ask follow-ups, receiving synthesized responses with links.
AI Mode is not the same thing as AI Overviews, though they’re closely related. AI Overviews are the AI-generated summaries that can appear on top of traditional results. Google positions AI Mode as a more end-to-end, conversational version—less like a summary perched above blue links, more like an interactive exchange.
AI Mode is not the same thing as AI Overviews, though they’re closely related. AI Overviews are the AI-generated summaries that can appear on top of traditional results. Google positions AI Mode as a more end-to-end, conversational version—less like a summary perched above blue links, more like an interactive exchange.
The part Google emphasizes (and the part readers should)
Google has been unusually explicit about both ambition and humility. In its own language, AI Mode is “early-stage” and “won’t always get it right.” That’s not a throwaway disclaimer. It’s a framing statement about what kind of system this is: one that synthesizes, infers, and sometimes guesses.
Readers should also notice what AI Mode is designed to excel at: open-ended tasks like comparing options, exploring trade-offs, or planning. Those are precisely the situations where your unstated assumptions matter most—and where AI’s “helpful” inferences can quietly steer you off course.
Readers should also notice what AI Mode is designed to excel at: open-ended tasks like comparing options, exploring trade-offs, or planning. Those are precisely the situations where your unstated assumptions matter most—and where AI’s “helpful” inferences can quietly steer you off course.
A simple mental model: search results vs. synthesized answers
Traditional Google Search largely ranks pages for a query. AI Mode does something more like: interpret your intent, break it into parts, retrieve across those parts, and then compose an answer. It’s still grounded in web results and Google systems, but the experience changes the burden of precision. You’re no longer just asking for sources—you’re asking for a constructed response.
AI Mode doesn’t only retrieve information. It interprets you—and interpretation is where assumptions enter.
— — TheMurrow
The mechanism behind “filling in the blanks”: query fan-out and intent lock-in
Google’s own documentation gives the clearest explanation for why AI Mode can feel like it’s answering a different question than the one you typed. The heart of it is a technique Google calls “query fan-out.”
According to Google, AI Mode issues multiple related searches concurrently across subtopics and multiple data sources, then synthesizes what it finds into a response. That’s powerful. It’s also a recipe for accidental drift when the original question is underspecified.
According to Google, AI Mode issues multiple related searches concurrently across subtopics and multiple data sources, then synthesizes what it finds into a response. That’s powerful. It’s also a recipe for accidental drift when the original question is underspecified.
What query fan-out does to your original question
Query fan-out means AI Mode isn’t treating your prompt as a single request with a single ranked list. It’s decomposing the prompt into sub-questions—some explicit, some implied.
If you ask: “Best laptop for remote work under $1,000,” AI Mode might fan out into:
- “Best laptops for Zoom/video calls”
- “Best battery life under $1,000”
- “Best lightweight laptops”
- “Best Windows laptops” (or “best Mac alternatives”)
- “Best value productivity laptops”
None of those are inherently wrong. The problem is that your real constraint might be “must run a specific engineering tool,” or “needs two external monitors,” or “must be available in my country.” Fan-out can amplify whatever interpretation the system chooses first.
If you ask: “Best laptop for remote work under $1,000,” AI Mode might fan out into:
- “Best laptops for Zoom/video calls”
- “Best battery life under $1,000”
- “Best lightweight laptops”
- “Best Windows laptops” (or “best Mac alternatives”)
- “Best value productivity laptops”
None of those are inherently wrong. The problem is that your real constraint might be “must run a specific engineering tool,” or “needs two external monitors,” or “must be available in my country.” Fan-out can amplify whatever interpretation the system chooses first.
Context carryover: the helpful feature that can harden a mistake
Google also says AI Mode carries context between consecutive queries to refine intent and formulate a more precise next step. In practice, that can create a subtle trap: if your first question is ambiguous, AI Mode may lock onto an interpretation and carry it forward.
A user who asks a follow-up—“What about the return policy?”—may assume they’re still talking about their preferred retailer or region. AI Mode may be operating on a different assumed context entirely.
A user who asks a follow-up—“What about the return policy?”—may assume they’re still talking about their preferred retailer or region. AI Mode may be operating on a different assumed context entirely.
Where AI Mode draws information from
Google says AI Mode can use:
- Real-time information from the web
- The Knowledge Graph (“info about the real-world”)
- Large-scale shopping data
Google has highlighted the scale of that shopping data in particular. In May 2025, Google described its Shopping Graph as spanning “over 45 billion product listings” and noted it updates “more than 2 billion product listings” every hour. Those are massive numbers—useful for breadth, but not a guarantee of accuracy for the one SKU you’re about to buy.
- Real-time information from the web
- The Knowledge Graph (“info about the real-world”)
- Large-scale shopping data
Google has highlighted the scale of that shopping data in particular. In May 2025, Google described its Shopping Graph as spanning “over 45 billion product listings” and noted it updates “more than 2 billion product listings” every hour. Those are massive numbers—useful for breadth, but not a guarantee of accuracy for the one SKU you’re about to buy.
45B+
Google said its Shopping Graph spans “over 45 billion product listings”—huge coverage, but not a promise your exact item details are correct.
2B+ / hour
Google said it updates “more than 2 billion product listings” every hour—scale that helps freshness, but doesn’t resolve ambiguous prompts.
Scale helps coverage. It doesn’t solve ambiguity.
— — TheMurrow
Why AI Mode’s assumptions can cost real money
The most consequential errors in AI Mode won’t look like errors. They’ll look like reasonable defaults.
When AI Mode fills in missing details, it tends to pick the most statistically plausible interpretation—not the one that matches your life. That’s fine for trivia. It’s expensive for purchasing, booking, and signing.
When AI Mode fills in missing details, it tends to pick the most statistically plausible interpretation—not the one that matches your life. That’s fine for trivia. It’s expensive for purchasing, booking, and signing.
Shopping: where a “helpful” guess becomes a bad purchase
Google explicitly markets AI Mode for product comparison and shopping-related queries. Shopping is also where users routinely omit constraints, because they don’t realize which detail is decisive until after they buy.
Common blank spaces AI Mode may fill:
- Price framing: pre-tax vs. after-tax; with fees vs. without
- Product condition: new vs. renewed/refurbished
- Variants and bundles: base model vs. upgraded; with accessories vs. without
- Compatibility: a “best” option that doesn’t fit your device or region
- Shipping and returns: availability and policies vary by seller and location
AI Mode can surface links, but a synthesized answer can lull readers into thinking the work of checking is done. For high-consideration purchases, that’s the wrong instinct.
Common blank spaces AI Mode may fill:
- Price framing: pre-tax vs. after-tax; with fees vs. without
- Product condition: new vs. renewed/refurbished
- Variants and bundles: base model vs. upgraded; with accessories vs. without
- Compatibility: a “best” option that doesn’t fit your device or region
- Shipping and returns: availability and policies vary by seller and location
AI Mode can surface links, but a synthesized answer can lull readers into thinking the work of checking is done. For high-consideration purchases, that’s the wrong instinct.
Travel: the quiet ambiguity tax
Google promotes AI Mode for trip planning and exploratory tasks. Travel is full of variables that humans frequently leave unstated: departure airport, date flexibility, baggage needs, time zones, cancellation rules, and hidden fees.
A plan that assumes the wrong airport or wrong date range can waste hours. A plan that glosses over cancellation policies or resort fees can cost more than the airfare.
A plan that assumes the wrong airport or wrong date range can waste hours. A plan that glosses over cancellation policies or resort fees can cost more than the airfare.
Services and fine print: when “compare options” becomes “miss a constraint”
Google has pointed to use cases like assessing insurance options. That category is a reminder that not all “search” is shopping; some searches are quasi-legal decisions.
Insurance, contracts, and regulated services often hinge on exclusions, jurisdiction, and edge cases. AI Mode may be excellent at summarizing typical differences while missing the one clause that matters to your situation. The system can provide links, but the reader must still treat the output as a starting map—not a final decision.
Insurance, contracts, and regulated services often hinge on exclusions, jurisdiction, and edge cases. AI Mode may be excellent at summarizing typical differences while missing the one clause that matters to your situation. The system can provide links, but the reader must still treat the output as a starting map—not a final decision.
The uncomfortable truth: AI answers can be confidently wrong
The best-known public examples of Google’s generative search failures have involved AI Overviews, not AI Mode. Still, the relevance is direct: Google describes AI Mode and AI Overviews as related experiences, sharing underlying generative behavior. If AI-generated summaries can fail in visible ways, a more conversational, end-to-end AI search experience can also fail—sometimes more subtly.
Google’s own language is the most important evidence here. The company explicitly warns that AI Mode “won’t always get it right.” That’s not speculation. It’s a statement from the maker about expected limitations.
Google’s own language is the most important evidence here. The company explicitly warns that AI Mode “won’t always get it right.” That’s not speculation. It’s a statement from the maker about expected limitations.
Why “confidently wrong” is different from “sometimes wrong”
Traditional search results can be wrong, too, but they’re plural: multiple sources, multiple framings, and a list that invites comparison. A synthesized answer creates a single narrative voice. Even with citations, the writing can feel authoritative in a way a ranked list does not.
The practical consequence is psychological: users may stop cross-checking when the answer looks coherent.
The practical consequence is psychological: users may stop cross-checking when the answer looks coherent.
Multiple perspectives: a tool, not an oracle
AI Mode’s defenders will reasonably argue that it’s still grounded in web information and that it provides links precisely so users can verify. That’s true, and it’s a meaningful design choice. A conversational interface can also help users ask better follow-ups and clarify their own needs.
Skeptics will counter that few people click through when the summary sounds complete, and that intent drift is uniquely hazardous in commerce and policy questions. Both perspectives can be true at once: AI Mode can reduce friction and increase the risk of quietly wrong assumptions.
Skeptics will counter that few people click through when the summary sounds complete, and that intent drift is uniquely hazardous in commerce and policy questions. Both perspectives can be true at once: AI Mode can reduce friction and increase the risk of quietly wrong assumptions.
Key Insight
The hardest AI Mode mistakes to spot are the ones that look like reasonable defaults—because they’re written cleanly and sound complete.
How to prompt AI Mode so it stops inventing your requirements
If AI Mode fills in the blanks, your best defense is to remove the blanks. That doesn’t mean writing a novel. It means stating the constraints that change the outcome.
The “five constraints” checklist
Before you hit enter, include:
- Location: country/state/city, or “online only”
- Budget definition: “under $800 before tax,” “under $1,000 total,” or “excluding subscription fees”
- Timeframe: “buying this week,” “traveling July 10–14,” “latest models as of 2025”
- Must-haves: compatibility, size, policy requirements
- Dealbreakers: “no refurbished,” “no third-party sellers,” “must have free cancellation”
A short prompt with constraints beats a long prompt without them.
- Location: country/state/city, or “online only”
- Budget definition: “under $800 before tax,” “under $1,000 total,” or “excluding subscription fees”
- Timeframe: “buying this week,” “traveling July 10–14,” “latest models as of 2025”
- Must-haves: compatibility, size, policy requirements
- Dealbreakers: “no refurbished,” “no third-party sellers,” “must have free cancellation”
A short prompt with constraints beats a long prompt without them.
Five constraints to include before you hit enter
- ✓Location (country/state/city, or “online only”)
- ✓Budget definition (before tax vs total; with fees vs without)
- ✓Timeframe (buying this week; dates; “as of 2025”)
- ✓Must-haves (compatibility, size, policy requirements)
- ✓Dealbreakers (no refurbished; no third-party sellers; free cancellation)
Ask AI Mode to show its assumptions
When you suspect AI Mode is inferring, ask it to surface the inference. Useful follow-ups:
- “What assumptions did you make about my location, budget, and timeframe?”
- “List the constraints you used to choose these recommendations.”
- “If I’m in Canada/UK/EU, how does your answer change?”
- “Which sources support each claim? Link them.”
These are not magic spells, but they change the tone of the interaction. They push the system toward explicitness.
- “What assumptions did you make about my location, budget, and timeframe?”
- “List the constraints you used to choose these recommendations.”
- “If I’m in Canada/UK/EU, how does your answer change?”
- “Which sources support each claim? Link them.”
These are not magic spells, but they change the tone of the interaction. They push the system toward explicitness.
Force comparisons that match your reality
Instead of asking, “What’s the best…,” ask for a bounded comparison:
- “Compare Option A vs Option B for my use case.”
- “Give me three recommendations: best value, best durability, best for X software—each under $Y total.”
- “Show trade-offs and what I give up with each.”
AI Mode is designed for reasoning. Give it a reasoning-shaped task.
- “Compare Option A vs Option B for my use case.”
- “Give me three recommendations: best value, best durability, best for X software—each under $Y total.”
- “Show trade-offs and what I give up with each.”
AI Mode is designed for reasoning. Give it a reasoning-shaped task.
Prompting principle
Remove ambiguity early (constraints), then force explicitness (assumptions + sources), then ask for trade-offs (bounded comparisons).
Three real-world scenarios where “blank filling” goes wrong—and how to prevent it
A good way to understand AI Mode’s failure modes is to picture ordinary, high-stakes searches where one missing detail changes everything.
Scenario 1: Buying electronics with compatibility landmines
A shopper asks for the “best wireless earbuds under $150.” AI Mode fans out into sound quality, battery, call performance, and popularity. It might default to mainstream picks.
But earbuds can be a compatibility puzzle: multipoint pairing, codec support, latency for gaming, fit, platform-specific features, and return policies that differ by seller. A single assumption—like prioritizing “most popular”—can drown out the user’s real need (say, low-latency for a specific device).
Prevention prompt: “Under $150 total, must support multipoint, prioritized for Windows laptop calls, and must be new (not renewed).”
But earbuds can be a compatibility puzzle: multipoint pairing, codec support, latency for gaming, fit, platform-specific features, and return policies that differ by seller. A single assumption—like prioritizing “most popular”—can drown out the user’s real need (say, low-latency for a specific device).
Prevention prompt: “Under $150 total, must support multipoint, prioritized for Windows laptop calls, and must be new (not renewed).”
Scenario 2: Planning travel where totals are not totals
A reader asks AI Mode for “a three-day beach trip” with a budget. AI Mode may suggest hotels and flights based on typical pricing. The trap is hidden fees and mismatched framing: “per night” vs. total; taxes not included; resort fees; baggage; or an airport that’s technically nearby but practically not.
Prevention prompt: “Total budget $1,200 including taxes/fees, flying from JFK, dates Aug 2–5, include one checked bag, and include cancellation policy details.”
Prevention prompt: “Total budget $1,200 including taxes/fees, flying from JFK, dates Aug 2–5, include one checked bag, and include cancellation policy details.”
Scenario 3: Comparing insurance options without the exclusions
Google itself has used examples like evaluating insurance options. AI Mode can summarize categories and typical differences, but policies often hinge on exclusions and jurisdiction. The danger isn’t that AI Mode will fabricate a policy; it’s that it will give a clean comparison that omits the clause you care about.
Prevention prompt: “Compare options focusing on exclusions, deductibles, and coverage limits; list what information you cannot determine from sources and what I must confirm with the provider.”
Prevention prompt: “Compare options focusing on exclusions, deductibles, and coverage limits; list what information you cannot determine from sources and what I must confirm with the provider.”
What publishers and businesses should learn from AI Mode’s design
AI Mode’s query fan-out has implications beyond individual users. It changes what “being found” means.
When AI Mode decomposes a query into subtopics, it can reward content that answers narrow questions clearly—return policy details, compatibility lists, fee breakdowns, region-specific availability. For publishers, specificity becomes a competitive advantage. For businesses, clear structured information becomes part of customer service.
Google says AI Mode draws from real-time web information, the Knowledge Graph, and shopping data. That blend increases the premium on consistency. If your pricing page says one thing and your shopping feed says another, AI Mode may synthesize a third version that satisfies neither.
A fair counterpoint: AI Mode can send traffic via links, and a well-cited answer may introduce readers to sources they’d never find in a traditional list. The opportunity is real. The risk is also real: if AI Mode summarizes your content incorrectly, the correction burden falls on you and the reader.
When AI Mode decomposes a query into subtopics, it can reward content that answers narrow questions clearly—return policy details, compatibility lists, fee breakdowns, region-specific availability. For publishers, specificity becomes a competitive advantage. For businesses, clear structured information becomes part of customer service.
Google says AI Mode draws from real-time web information, the Knowledge Graph, and shopping data. That blend increases the premium on consistency. If your pricing page says one thing and your shopping feed says another, AI Mode may synthesize a third version that satisfies neither.
A fair counterpoint: AI Mode can send traffic via links, and a well-cited answer may introduce readers to sources they’d never find in a traditional list. The opportunity is real. The risk is also real: if AI Mode summarizes your content incorrectly, the correction burden falls on you and the reader.
March 2025
Google introduced AI Mode as an experiment inside Search in March 2025, initially tested through Labs.
May 2025
Google expanded AI Mode’s visibility in the U.S. around May 2025, during the Google I/O timeframe.
TheMurrow take: treat AI Mode as a smart assistant with a bad habit
AI Mode can be genuinely useful. Google built it for complex reasoning, multi-part questions, and follow-ups, and it can pull from broad web information and large-scale shopping data. The experience is designed to reduce the labor of searching.
Still, Google’s own caveat matters: AI Mode is early-stage and won’t always be right. The system’s strength—query fan-out plus synthesis—is also the reason it can quietly answer the wrong question. Users who treat it like a final authority will be disappointed. Users who treat it like an eager assistant—fast, capable, sometimes presumptive—will get more value with fewer costly surprises.
The future of search may be conversational. For now, the best skill is not “prompt engineering.” It’s plain-language specificity: say what you mean, state your constraints, and make the system show its work.
Still, Google’s own caveat matters: AI Mode is early-stage and won’t always be right. The system’s strength—query fan-out plus synthesis—is also the reason it can quietly answer the wrong question. Users who treat it like a final authority will be disappointed. Users who treat it like an eager assistant—fast, capable, sometimes presumptive—will get more value with fewer costly surprises.
The future of search may be conversational. For now, the best skill is not “prompt engineering.” It’s plain-language specificity: say what you mean, state your constraints, and make the system show its work.
AI Mode can reduce friction and increase the risk of quietly wrong assumptions.
— — TheMurrow
T
About the Author
TheMurrow Editorial is a writer for TheMurrow covering how-to / guides.
Frequently Asked Questions
What is Google AI Mode, exactly?
AI Mode is a conversational, AI-generated experience inside Google Search designed for complex questions and follow-up queries. Google introduced it as an experiment in March 2025 and signaled broader U.S. visibility around May 2025. It responds with synthesized answers and includes links to web sources, rather than only showing a traditional ranked list.
How is AI Mode different from AI Overviews?
AI Overviews are AI-generated summaries that can appear at the top of traditional search results. AI Mode is more end-to-end conversational: you ask a question, receive an AI-generated response, and can continue with follow-ups that carry context. Google positions AI Mode as its “most powerful AI search” experience, while Overviews sit within classic Search.
Why does AI Mode sometimes answer a different question than I asked?
Google says AI Mode uses query fan-out, meaning it runs multiple related searches across subtopics and data sources, then synthesizes an answer. If your prompt is ambiguous, AI Mode may infer missing details—location, budget definition, timeframe, or “best for whom”—and then build the response around those assumptions.
What kinds of searches are most risky for wrong assumptions?
High-risk categories are the ones where ambiguity becomes expensive:
- Shopping (compatibility, bundles, refurbished vs new, shipping/returns)
- Travel planning (dates, airports, fees, cancellation policies, time zones)
- Services with fine print (Google has cited examples like insurance options)
In these areas, a “reasonable default” can still be wrong for your situation.
How can I stop AI Mode from “filling in the blanks”?
Remove the blanks upfront. Include location, timeframe, and a budget definition (before tax vs total, with fees vs without). Add must-haves and dealbreakers like “no refurbished” or “must have free cancellation.” You can also ask: “What assumptions did you make?” to force the system to be explicit.
Does AI Mode use real-time web information or just a model’s memory?
Google says AI Mode can draw on real-time information from the web, along with Google systems like the Knowledge Graph and large-scale shopping data. Google has highlighted the Shopping Graph’s scale—over 45 billion product listings and more than 2 billion listings updated every hour—as part of how it supports shopping queries.
