Query Fan-Out Architecture: From One Grocery Search to an AI Research Tree (2026 Guide)
A shopper asks:
What should I buy for a healthy taco dinner for six from a store near me that stays open late and has good deals this week?
Traditional search might attempt to find:
one page matching that sentence.
An AI-assisted retrieval system can approach the problem differently.
It may investigate:
Healthy taco ingredients
Protein options
Tortillas
Produce
Current grocery promotions
Nearby grocery locations
Store hours
Product availability
Prices
Dietary alternatives
Instead of performing:
one search
the system can potentially perform:
many related searches around the original information need.
That is the basic idea behind query fan-out.
Google now publicly uses this exact term for AI Mode. Google says AI Mode divides a question into subtopics and searches for each one simultaneously across multiple data sources, then brings the results together into a response.
Google has also described query fan-out as breaking a question into subtopics and issuing numerous related searches concurrently, allowing AI Mode to explore more of the web than a single traditional query might.
OpenAI describes a related—but not identically named—behavior for ChatGPT Search. ChatGPT may rewrite a user's request into one or more targeted searches and, after examining initial results, issue additional and more-specific searches.
Those systems should not be assumed to use:
the same internal architecture.
But they illustrate a major change in search:
the visible user query may be only the root of the retrieval process.
Behind it may be:
several searches, subtopics, refinements, candidate sources, and follow-up retrieval steps.
For grocery retailers, this matters because a shopper may never search:
FreshTown Downtown bakery custom cake ordering page
directly.
They may instead ask:
Where can I get a birthday cake near me for Saturday?
An AI system can potentially turn that request into a family of searches involving:
Nearby grocery stores
Bakeries
Custom cakes
Ordering requirements
Saturday hours
Pickup options
Locations
The store therefore does not merely need:
one page optimized for one query.
It needs:
an information architecture capable of answering the branches that may emerge from a broader shopper question.
That leads to the central principle of this guide:
The shopper asks one question. The retrieval system may investigate many.
And the defining architecture principle is:
Query fan-out turns a search into a research tree.
Table of Contents
Why Query Fan-Out Matters
One Search vs Many Searches
Why Complex Grocery Questions Create Fan-Out
The Root Query
Parallel Retrieval
Iterative Retrieval
Result Gathering
Evidence Consolidation
Synthesis
The Grocery AI Research Tree
Product Branches
Price Branches
Inventory Branches
Location Branches
Store-Hours Branches
Department Branches
Service Branches
Recipe Branches
Dietary Branches
Promotion Branches
Comparison Branches
Recommendation Branches
Query Fan-Out vs Query Decomposition
Query Fan-Out vs Intent Classification
Query Fan-Out vs Query Rewriting
Query Fan-Out vs HyDE
Query Fan-Out vs Semantic Retrieval
Query Fan-Out vs Hybrid Retrieval
Query Fan-Out vs Multi-Hop Retrieval
Query Fan-Out vs Reranking
Query Fan-Out vs MMR
Query Fan-Out and RAG
Query Fan-Out and Context Selection
Query Fan-Out Across Data Sources
Query Fan-Out and Entity Resolution
Query Fan-Out and Local Grocery Search
Query Fan-Out and Multi-Location Grocery Chains
Query Fan-Out and Dynamic Grocery Information
Query Explosion
Redundant Branches
Branch Drift
Constraint Loss
Contradictory Evidence
Missing Branches
The Grocery Fan-Out Architecture
The Grocery Research Tree
The BRANCH Framework
What Grocery Marketers Can Control
What Grocery Marketers Cannot Control
How Google Uses Query Fan-Out
What We Know About ChatGPT Search
Common Query Fan-Out Myths
Grocery Query Fan-Out Optimization Checklist
Frequently Asked Questions
Final Thoughts
What Is Query Fan-Out?
Query fan-out is a retrieval strategy where:
one user question leads to multiple related searches.
Instead of:
Query → Search → Results
the architecture becomes more like:
Query → Interpret → Expand → Search Multiple Branches → Gather Evidence → Synthesize
Google describes this explicitly in AI Mode.
According to Google's current Search help documentation, AI Mode uses a query fan-out technique that divides a question into subtopics and searches each one simultaneously across multiple data sources before combining the information into a response.
That means the visible question is:
not necessarily the only search query.
Why Query Fan-Out Matters
Imagine the shopper asks:
What's the best grocery store near me for a vegetarian cookout?
There may be no single webpage titled:
Best Grocery Store Near Me for a Vegetarian Cookout.
Yet the answer could be assembled from evidence about:
Nearby locations
Plant-based burgers
Produce
Vegetarian prepared foods
Grillable vegetables
Store hours
Pickup
Pricing
Current promotions
A system capable of fan-out can:
explore those dimensions separately.
This expands the search surface.
The retailer can potentially become relevant to the answer through:
several different branches.
One Search vs Many Searches
Traditional conceptual model:
QUESTION → SEARCH → RESULTS
Fan-out model:
QUESTION↓SUBTOPIC A → SEARCHSUBTOPIC B → SEARCHSUBTOPIC C → SEARCHSUBTOPIC D → SEARCH↓RESULT POOL↓EVIDENCE↓ANSWER
This is a profound architectural change.
The retrieval process is no longer necessarily:
one-dimensional.
It becomes:
exploratory.
Why Complex Grocery Questions Create Fan-Out
Grocery shopping naturally creates:
multi-variable questions.
For example:
Where can I buy an affordable high-protein breakfast near work tomorrow morning?
Potential dimensions:
Breakfast products
High-protein foods
Price
Nearby stores
Work location
Morning hours
Availability
Prepared food vs packaged food
A single broad query may not retrieve:
enough evidence.
Fan-out gives the system:
multiple ways into the information space.
The Root Query
The root query is:
the shopper's original question.
Example:
Where should I shop for taco night?
Everything downstream should remain:
connected to that root objective.
This matters because a system can easily generate:
interesting but irrelevant branches.
The root query provides:
the relevance anchor.
Subtopics
The first conceptual expansion is often:
subtopics.
For:
Where should I shop for taco night?
subtopics might include:
Taco ingredients
Tortillas
Meat
Produce
Salsa
Mexican grocery specialties
Price
Location
Store hours
Subtopics describe:
areas of information.
They are not necessarily:
final search queries.
Subqueries
A subtopic can become:
an executable search.
Examples:
grocery stores near [location]
stores with fresh tortillas near [location]
grocery taco meat deals this week
stores with Mexican grocery departments near [location]
These are:
subqueries.
The distinction:
Subtopic = information area.
Subquery = search expression used to investigate it.
Retrieval Branches
Each subquery can create:
its own retrieval branch.
Imagine:
Root
Best grocery store for taco night?
Branch 1
Nearby stores
Branch 2
Tortilla availability
Branch 3
Meat department
Branch 4
Produce
Branch 5
Mexican grocery selection
Branch 6
Weekly deals
These branches can potentially:
converge on the same retailer.
That convergence can make the retailer:
especially well-supported for the original answer.
Parallel Retrieval
One of the defining properties Google publicly describes is:
simultaneous searching.
Google says AI Mode can divide a question into subtopics and search each one at the same time across multiple data sources.
For a grocery query:
healthy dinner under $25 near me
parallel branches could investigate:
healthy dinner ideas
current grocery prices
stores nearby
weekly promotions
all at roughly:
the same retrieval stage.
This reduces the need to wait for:
one branch before starting another.
Iterative Retrieval
Not all retrieval has to be:
simultaneous.
Some systems may examine initial results and decide:
another search is necessary.
OpenAI explicitly describes this pattern.
ChatGPT Search may first issue a targeted search, inspect the returned information, and then send:
a more-specific follow-up query.
Conceptually:
Search → Learn → Refine → Search Again
This creates:
a growing research tree.
Result Gathering
Once several branches return results, the system may have:
many candidate sources.
For example:
Location branch
Store A
Store B
Store C
Vegetarian branch
Store A
Store D
Promotion branch
Store A
Store B
Prepared-food branch
Store A
Store C
Store A appears across:
several independent information branches.
That may provide:
stronger evidence coverage.
But branch frequency alone does not automatically equal:
final ranking or recommendation.
Evidence Consolidation
The system now has to:
combine overlapping information.
This can involve:
Deduplication
Entity resolution
Source evaluation
Freshness checks
Relevance ranking
Contradiction handling
Fan-out increases:
breadth.
But breadth increases:
complexity.
This is why:
fan-out is not the end of retrieval—it creates the evidence problem that later stages must solve.
Synthesis
The final answer should:
return to the original root question.
Suppose the original query was:
Which nearby grocery store is best for a vegetarian cookout?
The answer should not become:
disconnected explanations of grilling vegetables, grocery hours, plant-based meat, and produce.
Those findings need to be:
synthesized.
So:
Fan-out expands. Synthesis contracts.
That is one of the strongest concepts in the article.
The Grocery AI Research Tree
Consider:
Where should I shop for a healthy family dinner tonight?
The research tree could conceptually become:
ROOT QUERYHealthy family dinner tonight ↳ Meal BranchHealthy dinner ideas ↳ Serving BranchFamily-size meals ↳ Product BranchRequired ingredients ↳ Location BranchNearby grocery stores ↳ Hours BranchStores open tonight ↳ Price BranchAffordable options ↳ Promotion BranchCurrent weekly specials ↳ Availability BranchProducts in stock ↓EVIDENCE MERGE↓FINAL RECOMMENDATION
This is:
an AI research tree.
Product Branches
Query:
Where can I find ingredients for ramen night?
Product-related branches may include:
Noodles
Broth
Eggs
Scallions
Mushrooms
Chili oil
Pork
Tofu
If a store publishes:
strong department and product information,
multiple branches can independently:
discover it.
Price Branches
Query:
Where can I make taco night cheapest?
Price branches may investigate:
Ground beef
Tortillas
Cheese
Avocado
Salsa
The final answer may require:
basket-level comparison.
That introduces:
calculations and normalized product comparisons
beyond basic search.
Inventory Branches
Query:
Where can I buy everything for sushi tonight?
The answer may depend on:
Rice availability
Nori
Fish
Avocado
Soy sauce
Wasabi
Store hours
Fan-out can identify:
separate inventory needs.
But real-time inventory still requires:
current data.
Location Branches
Local questions can generate:
geography-specific branches.
Query:
Where should I shop after work?
Potential branches:
Grocery stores near workplace
Distance
Travel time
Hours
Relevant departments
Pickup
Google's AI Mode can use multiple sources for complex searches, including real-world and shopping information.
Store-Hours Branches
Time introduces:
a separate retrieval dimension.
Query:
Where can I buy fresh seafood after 9?
Potential branches:
Stores with seafood
Seafood department hours
Store hours
Geography
Critically:
store hours and department hours may differ.
A good information architecture should make:
both explicit.
Department Branches
Query:
Where should I shop for a barbecue?
Potential department branches:
Meat
Produce
Bakery
Beer/beverage if relevant
Prepared foods
Grocery
Deli
Each branch can expose:
different parts of a retailer's website.
Service Branches
Query:
Where can I order food for a party?
Potential service branches:
Catering
Deli trays
Bakery cakes
Prepared foods
Online ordering
Pickup
Delivery
The shopper used:
one broad phrase.
Fan-out can investigate:
multiple service concepts.
Recipe Branches
Query:
What should I make with chicken that's on sale?
Potential branches:
Chicken promotions
Chicken recipes
Side dishes
Ingredients
Meal cost
This crosses:
commercial and informational content.
A retailer with both:
current promotions and useful recipes
can potentially satisfy:
more branches.
Dietary Branches
Query:
Where can I get food for a gluten-free cookout?
Potential branches:
Gluten-free buns
Gluten-free sauces
Meat
Plant-based options
Snacks
Desserts
But dietary requirements need:
explicit product evidence.
Fan-out does not convert:
semantically related products
into:
verified dietary-safe products.
Promotion Branches
Query:
What's a cheap dinner I can make this week?
Branches could include:
Current promotions
Recipe ideas
Sale proteins
Sale produce
Pantry staples
This shows how one promotional page can become:
part of a broader AI answer
even when the user never specifically searches:
“weekly ad.”
Comparison Branches
Query:
Is Store A or Store B better for a seafood dinner?
Potential branch categories:
Seafood selection A
Seafood selection B
Price A
Price B
Hours
Prepared sides
Reviews or other evidence where appropriate
The result becomes:
a comparative research project.
Recommendation Branches
Recommendation queries tend to generate:
broad evidence requirements.
Query:
What's the best grocery store for someone who cooks Asian food?
Potential branches:
Asian grocery products
Produce
Seafood
Rice
Noodles
Sauces
Specialty ingredients
Location
Price
No single page may own:
the entire answer.
This is exactly why:
topical and entity coverage matter.
Query Fan-Out vs Query Decomposition
The immediately preceding article covered:
query decomposition.
These concepts overlap but need:
strict differentiation.
Query Decomposition
Asks:
What smaller information tasks make up the original problem?
Query Fan-Out
Asks:
What searches can be issued across those subtopics to gather the needed evidence?
Example:
Original:
Find a grocery store near me with gluten-free pizza under $10 that's open late.
Decomposition identifies:
Store location
Gluten-free pizza
Price
Hours
Fan-out issues searches such as:
grocery stores near user
gluten-free pizza grocery
gluten-free pizza prices
grocery stores open late
The clean distinction:
Decomposition builds the research plan. Fan-out executes the search branches.
Query Fan-Out vs Intent Classification
Intent classification asks:
What is the shopper trying to accomplish?
Fan-out asks:
What related searches could gather evidence for that objective?
So:
Intent chooses the goal. Fan-out expands the investigation.
Query Fan-Out vs Query Rewriting
Query rewriting changes:
one search expression.
Example:
Can someone bring groceries to my car?
becomes:
grocery curbside pickup
Fan-out instead creates:
several searches.
For example:
Curbside grocery stores
Nearby locations
Pickup hours
Ordering instructions
OpenAI's current Search documentation makes clear that query rewriting can result in one or more targeted queries, and subsequent more-specific searches may follow.
Therefore:
rewriting and fan-out can overlap
without being:
identical concepts.
Query Fan-Out vs HyDE
HyDE generates:
a hypothetical answer or document
and embeds it to improve retrieval.
Fan-out generates:
multiple searches or branches.
So:
HyDE expands the semantic representation.
Fan-out expands the search space.
They could coexist.
Example:
Root query↓Fan-out into 4 subqueries↓HyDE on one difficult semantic branch↓Retrieve
Query Fan-Out vs Semantic Retrieval
Fan-out decides:
what searches to perform.
Semantic retrieval decides:
which information matches each search based on meaning.
Conceptually:
Root Query → Fan-Out → Subquery → Embedding → Semantic Retrieval
Each branch can potentially use:
its own semantic matching.
Query Fan-Out vs Hybrid Retrieval
Each branch could use:
Sparse retrieval
Dense retrieval
Hybrid retrieval
Structured lookup
Shopping data
Local data
Knowledge graph data
Google specifically says AI Mode can issue searches across:
multiple data sources.
Therefore:
fan-out concerns search breadth; hybrid retrieval concerns retrieval-method combination.
Query Fan-Out vs Multi-Hop Retrieval
Parallel fan-out:
searches independent branches simultaneously.
Multi-hop retrieval:
uses one result to determine the next retrieval.
Example:
Fan-Out
Search simultaneously for:
Taco meat
Tortillas
Salsa
Multi-Hop
Find stores with seafood counters.
Then retrieve closing times for those stores.
Then choose the latest-closing one.
Real research systems can use:
both.
Query Fan-Out vs Reranking
Fan-out expands:
candidates.
Reranking contracts:
candidates.
The architecture can look like:
1 Root Query → 8 Searches → 80 Candidates → Rerank → 10 Candidates
The numbers are illustrative.
This gives us:
Fan-out maximizes coverage. Reranking restores precision.
Query Fan-Out vs MMR
Fan-out can produce:
overlapping results.
For example, several branches may retrieve:
the same location page.
MMR can potentially help later select:
complementary evidence.
So:
Fan-out creates branch diversity.
MMR can create evidence diversity.
They solve:
different redundancy problems.
Query Fan-Out and RAG
A sophisticated RAG system may use:
multiple retrieval calls.
Instead of:
Question → Retrieve → Generate
the architecture becomes:
Question↓Fan-Out↓Search ASearch BSearch CSearch D↓Merge Results↓Filter / Rerank↓Select Evidence↓Ground↓Generate
This can improve:
evidence coverage
for questions where:
no single retrieval is enough.
Query Fan-Out and Context Selection
Fan-out creates an obvious problem:
too much information.
Suppose:
10 branches
each retrieve:
10 candidates.
Now:
100 pieces of information
may be available.
The final model probably should not receive:
all of them.
So fan-out increases the importance of:
Deduplication
Reranking
MMR
Evidence evaluation
Context selection
The principle:
The wider the retrieval tree, the more important the selection funnel becomes.
Query Fan-Out Across Data Sources
This is especially relevant to grocery search.
A single question may require:
Website Search
For:
Department descriptions
Recipes
Service pages
Product Data
For:
Product identity
Brand
Size
Inventory Data
For:
Current availability
Pricing Data
For:
Current price
Promotion Data
For:
Sale status
Local Data
For:
Address
Distance
Hours
This is why Google's description of fan-out across:
multiple data sources
is particularly meaningful for retail search.
Query Fan-Out and Entity Resolution
Fan-out can create:
entity confusion.
Suppose branches retrieve:
Downtown bakery
Northside pharmacy
Westside hours
A synthesis system might accidentally combine them into:
one fictional super-location.
This is a serious risk.
The rule:
Every branch must preserve entity ownership.
The system needs to know:
which fact belongs to which store.
Query Fan-Out and Local Grocery Search
Local grocery queries are naturally:
branch-heavy.
Query:
What's the best grocery store near me tonight?
Potential branches:
Nearby stores
Hours
Distance
Grocery selection
Reviews
Departments
Services
Promotions
Location is not merely:
another keyword.
It can act as:
a hard constraint across the entire research tree.
Query Fan-Out and Multi-Location Grocery Chains
For multi-location retailers, fan-out can expose:
location differences.
Suppose:
Downtown
Sushi
Bakery
Pickup
Northside
Pharmacy
Seafood
Delivery
Westside
International foods
Butcher
Bakery
A broad question such as:
Which FreshTown is best for a seafood dinner?
can generate branches around:
Seafood
Produce
Wine/beverages if relevant
Prepared sides
Hours
Distance
The answer should be based on:
location-specific evidence.
Not:
aggregate chain-level attributes.
Query Fan-Out and Dynamic Grocery Information
Many high-value grocery branches involve:
changing facts.
Examples:
Prices
Inventory
Promotions
Hours
Pickup availability
A page may rank well or be retrieved semantically yet still:
contain outdated state.
The rule:
Fan-out increases evidence coverage, but freshness still determines whether dynamic evidence is usable.
Query Explosion
One danger of fan-out is:
uncontrolled expansion.
Query:
What should I eat for dinner?
could theoretically create:
Hundreds of cuisines
Ingredients
Dietary paths
Recipes
Products
Locations
That would be:
inefficient.
A useful fan-out system needs:
boundaries.
Those can come from:
Intent
Constraints
Context
Relevance
Cost
Time
Confidence
This creates a core principle:
A research tree needs pruning as much as it needs branching.
Redundant Branches
Different generated searches may mean:
almost the same thing.
Example:
Grocery taco specials
Taco ingredient deals
Taco night promotions
Taco sale groceries
Those branches may retrieve:
nearly identical evidence.
The system may need:
deduplication.
This is another reason MMR and candidate fusion can matter:
downstream.
Branch Drift
A fan-out branch can move:
away from the root objective.
Root:
healthy breakfast under $10
Drifted branch:
gourmet breakfast restaurants
The branch is:
related to breakfast
but no longer satisfies:
Grocery context
Budget
Shopping intent
The principle:
Every branch needs a path back to the root query.
Constraint Loss
Consider:
gluten-free pizza under $10 at Downtown
A generated branch:
best pizza products
has lost:
Gluten-free
Under $10
Downtown
That branch may retrieve:
irrelevant evidence.
So:
Fan out the information need without fanning out away from its constraints.
Contradictory Evidence
Broader retrieval can increase:
conflict.
One branch may retrieve:
Downtown closes at 10.
Another:
Downtown closes at 9.
A third:
holiday hours differ.
The system now needs:
source resolution.
More retrieval does not automatically produce:
more certainty.
Sometimes:
it reveals disagreement.
Missing Branches
The opposite problem:
the fan-out never investigates an important dimension.
Query:
best grocery store for a family dinner under $30
Branches:
Recipes
Grocery stores
Product selection
Missing:
price.
The result cannot reliably satisfy:
the budget constraint.
This gives us:
Fan-out completeness depends on whether the research tree covers the facts required by the original question.
The Grocery Fan-Out Architecture
This should be the article's main technical visual.
ROOT QUERY
Where should I shop for a healthy taco dinner for six tonight?
↓
QUERY UNDERSTANDING
Meal
Healthy
Taco
Six people
Grocery shopping
Tonight
↓
FAN-OUT
Branch A — Meal
healthy taco dinner ingredients
Branch B — Protein
lean taco proteins
Branch C — Products
tortillas, salsa, produce, cheese
Branch D — Location
nearby grocery stores
Branch E — Hours
stores open tonight
Branch F — Promotions
taco ingredient deals this week
Branch G — Availability
products available by location
↓
RETRIEVAL
↓
MERGE
↓
DEDUPLICATE
↓
RESOLVE ENTITIES
↓
RERANK
↓
SELECT EVIDENCE
↓
SYNTHESIZE
↓
ANSWER
This becomes:
Root → Branch → Search → Gather → Prune → Merge → Answer
The Grocery Research Tree
The second major visual should show:
one root creating multiple levels.
Example:
ROOT
Best grocery store for taco night?
↓
LEVEL 1
Location
Products
Price
Hours
Services
↓
LEVEL 2
Under Products:
Tortillas
Protein
Produce
Cheese
Salsa
Under Services:
Pickup
Delivery
Under Price:
Weekly ad
Product pricing
↓
SOURCES
Location pages
Product catalog
Department pages
Promotion pages
Inventory feeds
↓
EVIDENCE
↓
FINAL ANSWER
This should visually communicate:
AI search can behave more like research than lookup.
The BRANCH Framework
This should be the Grocery Growth marketer-facing framework.
It is not:
Google's fan-out algorithm.
It is a content-readiness model.
B — Build Strong Topic Branches
Create substantive coverage around:
Locations
Departments
Products
Services
Recipes
Promotions
R — Relate Every Fact to an Entity
State clearly:
who or what owns the fact.
Example:
Downtown → Bakery → Custom Cakes
Not merely:
Custom cakes available.
A — Answer Distinct Information Needs
Provide clear answers about:
Price
Hours
Availability
Services
Products
Policies
Uses
N — Normalize Important Constraints
Make important conditions explicit:
Location
Date
Price
Size
Diet
Availability
C — Connect the Branches
Use logical:
Internal links
Breadcrumbs
Navigation
Related content
Entity relationships
So a system—and shopper—can move:
across the topic.
H — Hold Information Current
Dynamic branches require:
freshness.
Keep updated:
Prices
Promotions
Hours
Inventory
Services
So:
BRANCH = Build → Relate → Answer → Normalize → Connect → Hold Current
The marketer-facing principle:
You do not need a page for every possible AI query. You need a strong information network capable of satisfying the branches behind those queries.
What Grocery Marketers Can Control
Grocery retailers cannot control:
the fan-out tree generated by an external AI platform.
But they can control whether branches have:
useful destinations.
For example:
Brand
Clear brand information.
Locations
Dedicated location pages.
Departments
Detailed department pages.
Services
Explicit service information.
Products
Structured, descriptive product information.
Promotions
Searchable, current offer pages.
Recipes
Useful educational content.
Relationships
Clear internal linking among:
brand → location → department → service → product.
This is where topical authority becomes:
operationally useful.
What Grocery Marketers Cannot Control
On external platforms, marketers generally cannot control:
Whether fan-out occurs
Number of branches
Branch wording
Branch order
Query-generation model
Search providers
Subtopics
Candidate counts
Retrieval source
Branch weighting
Branch pruning
Search depth
Reranker
Final synthesis
Therefore:
do not optimize for imaginary fan-out queries.
Optimize:
the underlying information landscape.
How Google Uses Query Fan-Out
Google is unusually explicit about:
this particular technique.
Google says AI Mode:
divides a question into subtopics
and searches for those subtopics:
simultaneously across multiple data sources.
Google has also described the technique as issuing:
many simultaneous queries on the user's behalf
to explore the web more deeply than a single traditional search.
Google's AI Mode announcement further explains that the system can handle complex, multi-part questions and use query fan-out across subtopics and multiple data sources before bringing the results together.
This makes query fan-out:
one of the clearest publicly documented mechanisms in modern generative Search.
But there are still important limits.
Google does not publicly provide publishers with:
Exact generated fan-out queries for every search
Number of branches
Branch weighting
Retrieval depth
Branch selection score
Universal fan-out formula
And Google does not tell publishers:
create one page for every possible branch.
The useful takeaway is:
build comprehensive, connected information that can remain relevant across multiple related retrieval paths.
What We Know About ChatGPT Search
OpenAI does not currently label its documented behavior:
“query fan-out.”
But ChatGPT Search can exhibit conceptually related multi-search behavior.
OpenAI says ChatGPT Search typically rewrites requests into:
one or more targeted queries
when working with search providers.
After reviewing the first results, ChatGPT may also send:
additional, more-specific searches.
OpenAI's example shows a broad biotechnology request first becoming a targeted search, with another more-specific query issued after the system reviews what it finds.
Therefore it is accurate to say:
ChatGPT Search can use multiple targeted and iterative searches.
It would be inaccurate to say:
ChatGPT Search publicly confirms Google's exact query fan-out architecture.
Those are:
different claims.
Do not state:
ChatGPT universally uses Google-style query fan-out
ChatGPT creates a fixed research tree
ChatGPT has a publisher-visible branch score
OAI-SearchBot performs a known fan-out algorithm
Google and OpenAI use identical retrieval architectures
Common Query Fan-Out Myths
“Query fan-out means Google searches the same phrase multiple times.”
No.
Google says it:
divides a question into subtopics
and searches across them.
“Query fan-out means every user query becomes dozens of searches.”
Not necessarily.
The exact number and behavior are:
not publicly fixed.
“Every search needs fan-out.”
No.
Simple questions may not require:
broad research.
“Fan-out and query decomposition are identical.”
No.
They overlap.
But:
decomposition defines information tasks.
Fan-out executes:
multiple related searches.
“Fan-out is the same as query rewriting.”
No.
Rewriting may produce:
one improved search.
Fan-out produces:
multiple search paths.
“Fan-out is HyDE.”
No.
“Fan-out is vector search.”
No.
“Fan-out is hybrid retrieval.”
No.
“Fan-out is reranking.”
No.
“Fan-out is RAG.”
No.
It can be:
part of a retrieval process supporting RAG.
“More branches automatically improve answers.”
No.
More branches can create:
Noise
Duplication
Conflict
Latency
“Every branch has equal importance.”
Not necessarily.
“Fan-out automatically preserves constraints.”
No.
Constraint loss is:
a failure mode.
“Fan-out verifies facts.”
No.
“Fan-out ensures current information.”
No.
“Fan-out prevents hallucinations.”
No.
“A site has to rank #1 for the original query to appear in AI search.”
Not necessarily.
Query fan-out means a page may potentially become relevant to:
one of several related retrieval branches
rather than only:
the literal root query.
That does not guarantee:
inclusion in the final response.
“You should create a page for every possible fan-out query.”
No.
That can create:
thin, repetitive content.
The better strategy is:
strong topical and entity coverage.
“Google publishes every fan-out query.”
No.
“Google has a fan-out ranking score.”
No public publisher-facing score is documented.
“ChatGPT officially calls its behavior query fan-out.”
Not in the cited Search documentation.
OpenAI describes:
targeted query rewriting and additional searches.
“There is query fan-out schema markup.”
No.
Grocery Query Fan-Out Optimization Checklist
Topic Coverage
Brand information complete
Location pages complete
Department pages useful
Services explicit
Product information descriptive
Promotions searchable
Recipes cover real shopper needs
Entity Relationships
Brand → Location clear
Location → Department clear
Department → Service clear
Location → Service clear
Product → Department clear where appropriate
Promotion → Product clear
Promotion → Location clear
Constraints
Location explicit
Price explicit
Product size explicit
Dietary attribute explicit
Effective dates explicit
Hours explicit
Availability current where supported
Internal Architecture
Related pages internally linked
Orphan pages minimized
Breadcrumb structure clear
Location pages link to departments/services
Department pages link to relevant content
Editorial content links to commercial pages naturally
Content Quality
Distinct pages serve distinct purposes
Thin query-variation pages avoided
Real shopper questions answered
First-party information included
Useful local specificity included
Content kept current
If Building Your Own Fan-Out System
Root query preserved
Intent understood
Constraints extracted
Subtopic generation tested
Duplicate branches controlled
Branch drift measured
Parallel retrieval supported where useful
Sequential retrieval supported where required
Candidate fusion evaluated
Entity resolution evaluated
Reranking evaluated
MMR evaluated
Context selection evaluated
Latency measured
Cost measured
Final answer accuracy measured
Claims Discipline
No invented number of Google fan-out queries
No claim every Google query uses fan-out
No claim Google exposes fan-out weights
No claim ChatGPT uses Google's exact architecture
No claim fan-out guarantees citation
No claim fan-out replaces SEO
No page created solely for every imagined branch
Frequently Asked Questions
What is query fan-out?
Query fan-out is a technique where:
one user question generates multiple related searches across subtopics in order to gather broader evidence.
Google publicly uses this term for AI Mode.
Does Google use query fan-out?
Yes.
Google says AI Mode divides questions into subtopics and searches for each one simultaneously across multiple data sources.
What does “fan-out” mean?
It refers to:
one starting point expanding into multiple downstream paths.
Visually:
1 → many
Is query fan-out new?
Multi-query and query-expansion approaches are not new to information retrieval.
What is especially important now is:
Google's explicit use of query fan-out terminology in its generative Search products.
What is a fan-out query?
It is useful shorthand for:
one of the related searches generated from the broader original information need.
How is query fan-out different from normal search?
A traditional simplified model might use:
one search query.
Fan-out can issue:
several related searches.
What is a query fan-out tree?
A query fan-out tree is a conceptual representation of:
the root question and the searches/subtopics that branch from it.
It is useful for explaining:
retrieval architecture.
It should not be presented as Google's literal exposed internal UI or data structure.
Does Google call it a research tree?
No.
“AI research tree” is:
a Grocery Growth explanatory concept.
Google publicly calls the mechanism:
query fan-out.
Does fan-out happen in parallel?
Google specifically says AI Mode searches:
subtopics simultaneously.
Other systems may also use:
sequential or iterative retrieval.
Does ChatGPT Search perform multiple searches?
Yes.
OpenAI says ChatGPT Search may rewrite a request into one or more targeted searches and may issue additional specific searches after reviewing initial results.
Does ChatGPT call that query fan-out?
The cited OpenAI documentation does not use that term.
Is query fan-out the same as query decomposition?
No.
A useful distinction is:
Decomposition = identify the information tasks.
Fan-Out = launch searches across those tasks or related subtopics.
Is query fan-out the same as query expansion?
They are related.
But generic query expansion can simply add:
terms or alternatives.
Fan-out can create:
multiple independent searches.
Is fan-out the same as HyDE?
No.
HyDE generates:
hypothetical document-like content
for embedding-based retrieval.
Can fan-out use dense retrieval?
Yes.
Can fan-out use sparse retrieval?
Yes.
Can fan-out use hybrid retrieval?
Yes.
Can different branches use different retrieval systems?
Yes, conceptually.
A price branch could use:
structured pricing data.
A recipe branch could use:
semantic document retrieval.
Can fan-out help complex grocery questions?
Potentially, especially when the question requires:
several distinct types of evidence.
Can fan-out help simple grocery questions?
It may not be necessary.
For:
What time does Downtown close?
one authoritative hours source may be enough.
Does fan-out improve recall?
It can broaden:
the candidate evidence space.
But its effectiveness depends on:
Generated subqueries
Retrieval quality
Coverage
Filtering
Reranking
Can fan-out reduce precision?
Yes.
More searches can retrieve:
more irrelevant candidates.
Can fan-out create duplicate results?
Yes.
Can fan-out lose important constraints?
Yes.
Can fan-out cause entity confusion?
Yes.
Especially in:
multi-location grocery search.
Can fan-out retrieve stale information?
Yes.
Freshness remains:
a separate concern.
Does fan-out determine the final answer?
No.
It is:
an upstream retrieval mechanism.
Does fan-out determine citations?
Not directly.
Retrieved evidence still needs:
evaluation and selection.
Should grocery stores optimize for fan-out?
Not by:
guessing hundreds of hidden queries.
Instead, build:
comprehensive, connected, explicit information around real shopper needs.
Final Thoughts
Traditional search encourages a simple mental model:
one query → one results page.
Generative AI search complicates that model.
The shopper may ask:
What's the best grocery store near me for a healthy family dinner tonight?
But the retrieval process may investigate:
Nearby stores
Healthy meals
Product options
Current promotions
Store hours
Availability
Services
Prices
Google has publicly confirmed that AI Mode uses query fan-out to divide questions into subtopics and search across them simultaneously.
This means modern search can behave more like:
research.
The original question becomes:
a starting point.
Subtopics become:
research branches.
Queries become:
retrieval probes.
Documents become:
evidence.
And the final AI response becomes:
a synthesis of information gathered across the tree.
This is why grocery retailers should not think exclusively in terms of:
“What keyword does this page rank for?”
A more advanced question is:
“What information need can this page satisfy inside a broader shopper journey?”
A department page can support:
a meat-counter branch.
A location page can support:
a local-hours branch.
A weekly-ad page can support:
a promotion branch.
A recipe can support:
a meal-planning branch.
A service page can support:
a curbside or catering branch.
A product page can support:
a dietary, price, product, or use-case branch.
And those branches can potentially:
converge around the same retailer.
That is why the Grocery AI Research Tree becomes:
Root Query → Subtopics → Subqueries → Retrieval Branches → Evidence → Synthesis
The Grocery Fan-Out Architecture becomes:
Root → Branch → Search → Gather → Prune → Merge → Answer
And the BRANCH Framework becomes:
Build → Relate → Answer → Normalize → Connect → Hold Current
Within the larger Grocery Growth retrieval architecture:
Shopper Query → Intent Resolution → Query Decomposition → Query Fan-Out → Query Rewriting / HyDE → Multi-Source Retrieval → Candidate Fusion → Filtering → Entity Resolution → Reranking → MMR → Evidence Evaluation → Context Selection → Grounding → Generation → Citation
This article therefore owns:
the multi-query search-expansion architecture layer.
Intent classification asks:
What does the shopper need?
Query decomposition asks:
What information tasks make up the problem?
Query fan-out asks:
What searches should investigate those tasks and related subtopics?
Retrieval asks:
What evidence can those searches find?
Reranking asks:
Which evidence matters most?
And synthesis asks:
How does everything discovered answer the original question?
The three defining lines for this article should be:
The shopper asks one question. The retrieval system may investigate many.
Query fan-out turns a search into a research tree.
Fan-out expands. Synthesis contracts.
Want Your Grocery Website to Be Discoverable Across More AI Search Branches?
Grocery Growth helps grocery retailers build connected information architectures around:
Locations
Departments
Services
Products
Promotions
Recipes
Local information
Shopper questions
Search intent
The objective is not:
guessing every query an AI system might generate.
It is ensuring that when a broad shopper question expands into multiple retrieval branches:
your grocery business has accurate, specific, connected information capable of satisfying those branches.
That can include semantic content architecture, location optimization, department pages, product information, internal linking, structured data, entity optimization, content freshness, LLM retrieval optimization, GEO, AEO, and SEO.
Explore Grocery Growth's SEO and AI-search optimization services.
Written by Grocery Growth
Grocery Growth helps grocery retailers build clearer digital information architectures for traditional search, semantic retrieval, AI-assisted discovery, and modern grocery shopping experiences.
Related Articles
Pre-Retrieval Architecture: Intent Classification Before Retrieval; Query Decomposition; HyDE: Hypothetical Document Embeddings.
Semantic Matching: Query–Document Semantic Similarity; Vector Embeddings Explained; Cosine Similarity and Semantic Retrieval.
Candidate Retrieval: Dense vs Sparse Retrieval; Hybrid Retrieval; HNSW Explained; Late Interaction Retrieval: ColBERT and Token-Level Matching.
Candidate Evaluation: Cross-Encoder Reranking; Maximum Marginal Relevance.
Pipeline Architecture: Multi-Stage Grocery Content Retrieval Pipelines.
Downstream AI: RAG and Grocery Store Websites; Grounding in AI Search; Citation Optimization.
Comments