Context Hints for ChatGPT Ads: The Definitive Guide (with 50 Examples)

Tarun Kapoor Updated October 14, 2026 31 min read

Context hints are short, plain-language descriptions of the conversations, situations and buyers where your ChatGPT ad is relevant. You set them on each ad group in OpenAI's Ads Manager, and they're the main targeting signal in ChatGPT Ads. They replace the cookie, the device graph and the literal-match keyword with one conversational instruction the model can reason about. This guide covers how to write them, how many to use, how to test them and how the auction scores them, with 50 examples you can adapt.

What context hints are

Inside OpenAI's Ads Manager Beta, the unit of targeting is an ad group. An ad group contains your ads, your bid, your budget pacing — and your context hints. OpenAI's own help-center definition is plain:

"At the ad group level, advertisers can provide context hints that describe the conversations, topics, or keywords where their products or services may be relevant. These hints help guide ad matching, but they are not exact-match keywords and do not guarantee delivery in specific conversations." — OpenAI Help Center, Ads in ChatGPT: The Basics

Three things matter in that definition. First, the hint is a description, not a token. Second, OpenAI explicitly says hints are not exact-match. Third, OpenAI explicitly says delivery is not guaranteed. Each of these is a meaningful break from the way paid-search advertisers have written keyword lists for the last twenty years, and each one changes how you should plan a campaign.

The simplest way to think about a context hint is this: imagine you could whisper a one-paragraph briefing to ChatGPT before every relevant conversation, telling it when your product is worth surfacing. The briefing describes the kind of question being asked, the kind of user asking it, and the kind of decision they are about to make. That briefing is your context hint.

Are context hints required?

No. The field is optional, and an ad group will save without it. But leaving it empty doesn't make your targeting broader. It just means the auction has less to go on. With no hints, the only relevance signals left are your ad title, ad copy and landing page, and those describe your offer, not the conversation you want to appear in. We treat an ad group with no hints as unfinished. If you're new to the platform, start with our complete ChatGPT Ads guide and how ChatGPT ads actually work, then come back here.

If you would rather have the hint set researched and built for you, that is our context hint strategy service. And if the goal is to be mentioned in ChatGPT’s organic answers rather than its ads, that is a different discipline: generative engine optimization.

Why context hints replaced keywords

The keyword-as-target model worked because search was a typed query box. The user wrote three words. The advertiser bid on three words. The platform matched the words. That symmetry collapses inside an LLM. A buyer who would have once typed best CRM for healthcare into Google now types a paragraph into ChatGPT:

"We're a Series B healthtech company in Boston, around 90 employees, mostly clinicians and a small RevOps team. We need a CRM that's HIPAA-eligible, integrates with our EHR for patient outreach, and isn't going to take six months to roll out. Salesforce feels heavy, but I don't want a fragile startup tool either. What's the right shortlist for us in 2026?"

That paragraph contains the company stage, the headcount, the geography, the buyer role, the regulatory constraint, the integration requirement, the timeline, the competitive comparison, the budget signal, and the desired outcome. Every one of those is a targeting parameter the old keyword model could not represent. The keyword best CRM for healthcare is in there technically, but if your campaign is built on that string alone, you've thrown away ninety percent of what the user actually told you.

A context hint captures the paragraph instead of the substring. It says something like: founders or RevOps leaders at Series A–C US healthtech companies (50–150 employees) evaluating HIPAA-eligible CRMs with EHR integration who want fast time-to-value and don't want Salesforce. That hint is a fingerprint of the conversation, not a token in it. The auction is then free to evaluate whether your ad belongs in this specific conversation based on how well the conversation fingerprint matches your hint fingerprint.

The five things context hints capture that keywords cannot

• Role and seniority. "VP of Marketing at a B2B SaaS" is a hint. It has never been a viable keyword.
• Company stage and size. "Series B with 50–200 employees" is a hint. The keyword search volume on those exact strings is effectively zero.
• Regulatory or industry constraint. "HIPAA-eligible," "SOC 2," "EU-only data residency" — keywords technically exist, but the user almost never types them as standalone queries.
• Funnel stage. "Evaluating a shortlist," "asking for a comparison," "ready to switch from a competitor" — these are intent states, not strings.
• Competitive context. "Considering switching away from HubSpot to something lighter" is a hint. As a keyword it is too long-tail to bid on profitably.

Where context hints live in Ads Manager Beta

The Ads Manager Beta object model nests three things:

That placement matters strategically. Because hints live at the ad group level, the cleanest way to scale a campaign is one ad group per audience-and-intent combination. If you sell to three different verticals and three different funnel stages, you build nine ad groups, each with its own set of context hints pointing at the right conversations.

Inside each ad group, run two or three ads that say genuinely different things. They should share a promise but differ in angle, so the system has real alternatives to test. Split into a new ad group when a group of hints needs a different message or a different landing page. The Ads Manager setup walkthrough shows where each field sits on screen. Account, brand and legal names covers the account-level settings you can't change later.

How ChatGPT picks which ad to show, and where hints fit

ChatGPT Ads has no keyword list for the platform to look up. Matching runs at three levels, and each one controls a different part of whether your ad can show.

1. The campaign decides who is eligible. Country, platform (iOS app, Android app or web) and custom audiences are all set on the campaign. OpenAI's developer docs say context hints "are not keywords and do not replace explicit geographic, platform, or audience targeting."
2. The ad group describes the conversation. This is where context hints live, alongside the bid. Your hints tell the system which conversations, needs and situations make this group of ads relevant.
3. The ad and landing page are judged together with the hints. OpenAI scores relevance across the hints, ad title, ad copy and landing page against the live conversation. It then ranks eligible ads by relevance and bid in a relevance-weighted auction.

Two mechanics from OpenAI's API docs matter when you manage hints at scale. First, an ad group can hold up to 2,000 hints. That is a ceiling, not a target, and the five-to-fifteen range below still applies. Second, sending a new hint list replaces the old one in full. To add a single hint through the API, fetch the current list, append the new hint and resubmit everything. If audiences are part of your plan, see our guide to ChatGPT Ads custom audiences. Inclusion audiences need at least 25,000 matched users, and when a user sits in both an inclusion and an exclusion list, the exclusion wins.

OpenAI's what, who, when guidance, mapped to Audience–Intent–Topic

OpenAI's help-center article on writing context hints organises the advice around three questions. What does your product do: its features, pricing, service area and availability. Who is it for: the needs and circumstances of your best-fit customers. When does it become useful: the situation or trigger that sends someone to ChatGPT asking for help. Our Audience–Intent–Topic framework, set out below, asks for the same three things in a different order:

Write situations, not categories

The most common practitioner advice, and the core of OpenAI's own guidance, comes down to one move: describe a scenario, not a product category. “Running shoes” is a category. “Beginners training for their first 5K who want cushioned everyday shoes that won't cause shin splints” is a situation. People don't open ChatGPT with a category. They open it with a problem, and the hint should read like that problem.

Need a starting point? The free context hint generator drafts situation-style hints from a short description of your product, and how to write context hints for ChatGPT Ads has fill-in templates for each funnel stage, twelve weak hints rewritten and three complete ad groups.

How the relevance-weighted second-price auction scores hints

OpenAI describes the auction as relevance-weighted, second-price. Both halves of that phrase matter.

The second-price half

In a second-price auction the winner pays roughly what it takes to beat the next-ranked ad, not their own max. The strategic implication is well-understood from twenty years of Google Search history: you should set your max bid at the true value of a click, because you will almost never pay it. Bidding shy is leaving impressions on the table; bidding aggressively but reasonably is the right move.

The relevance-weighted half

"Relevance-weighted" means the platform multiplies each bidder's bid by a relevance score before ranking them. The relevance score is computed across multiple inputs the help center calls out specifically: context hints, landing page, ad title, and ad copy. An advertiser with a $2.50 bid and a 0.9 relevance score will beat an advertiser with a $4.00 bid and a 0.4 relevance score in the ranked auction, and will likely pay a fraction of their max.

This is the single most important strategic insight in the entire system. You do not win this auction by outspending — you win it by being more relevant. Context hints are the largest lever you have to move the relevance side of the equation, because they are the only input that describes the conversation rather than the offer.

OpenAI has also said that memory and personalisation may come to inform ad relevance, with user controls. Today the buying model is still contextual: you're matched to the conversation, not to a profile. For the conversation-stage view of the same mechanics, see OpenAI Ads intent targeting, evaluated.

Anatomy of a great context hint

Across the campaigns we have audited at Context Hints, the best-performing hints share a structure. We call it the Audience — Intent — Topic framework. Every great hint names all three layers, in that order.

Layer 1: Audience

Describe the person asking. Role, company size, industry, geography, sometimes seniority. The richer you are, the easier it is for the model to score relevance high. "Founders" is weak. "Solo founders of pre-seed B2B SaaS companies in the US" is strong.

Layer 2: Intent

Describe what they are trying to do right now. "Buying" is too broad — buying what? At what stage? Switching from what? "Evaluating a shortlist of CRMs to switch to after outgrowing HubSpot Starter" is the kind of intent a model can match against. Three useful intent verbs that work across categories: evaluating, switching, comparing.

Layer 3: Topic

The category, sub-category, and any constraints. "CRM" is the category. "Modern CRM with usage-based pricing" is sub-category. "HIPAA-eligible CRM with EHR integration" is sub-category with constraint. The constraint is what disqualifies all the generic CRMs and qualifies yours.

The full template

[Audience: who they are, what they do, where they work]
+ [Intent: what they're trying to accomplish right now]
+ [Topic: the category, sub-category, and constraint that points at your product]

Worked example for a fictional HIPAA-eligible CRM called Cardiff:

And the same hint, weakened by stripping the layers:

The weak hint will match a thousand conversations a day. The strong hint will match thirty. The strong hint will win all thirty because its relevance score is unbeatable; the weak hint will lose most of its thousand because every other CRM advertiser in the auction is also bidding on it.

Writing context hints for each buyer stage

The same buyer sounds different in a learning conversation, a comparison and the moment before they decide. A single ad group that mixes all three blurs its relevance. Give each stage its own ad group, and keep the landing page matched to it. A reliable formula for one hint is who + situation + need + constraint.

The constraint is the part most hints leave out, and it often does the most work. Budget, team size, location, deadline and the tool someone is switching away from are all details a buyer states out loud in a chat. For more worked versions, see the fill-in templates in how to write context hints and the stage-by-stage view in OpenAI Ads intent targeting.

What context hints can't do (and where those controls live instead)

Hints describe conversations. Anything that describes a person or a place belongs in a different control, and putting it in a hint wastes the field.

Two more boundaries shape how you write. Advertisers never see the conversations they matched. Reporting is aggregated, so there is no search-terms report to mine. And ads don't change ChatGPT's answers. Being cited in the answer is a separate, organic discipline, covered in how to appear in ChatGPT answers.

What advertisers can and can't see

Context hints describe a conversation, but advertisers never see the conversation itself. That is the trade at the centre of ChatGPT Ads: the platform does the matching, and you get results back.

• What you get: delivery and performance numbers at the account, campaign, ad group and ad level. That means impressions, clicks, spend and CTR, plus conversions once you have conversion tracking in place.
• What you don't get: the prompts or chats that triggered your ad, and a search-terms-style report showing which hint matched. That is why testing hints means splitting them across ad groups rather than reading a per-hint report.
• Who never sees ads: OpenAI's help center says it does not show ads to users on Plus, Pro or any Business plan. The how ChatGPT ads work guide covers the rest of the eligibility rules.

For the measurement side, our guide to ChatGPT Ads conversion reporting shows which numbers the Insights API returns and how to read them.

How many context hints, and how long should each be?

• Per ad group: five to fifteen. Launch with five, then leave them alone for five to seven days before you judge the result. OpenAI doesn't publish a cap we'd plan around. The practical limit is coherence: every hint in a group should describe the same buyer in the same situation.
• Per hint: one idea, one or two sentences. If a hint needs “and” to join two audiences, it's two hints, and probably two ad groups.
• Treat every edit as a new set. Reporting belongs to the ad group, not the individual hint. Once you add, remove or rewrite hints, the before-and-after comparison is gone. Batch your changes and log the date.
• Write in the buyer's language, in the market's language. Hints for Japan, Korea, Brazil or the newer Southeast Asian markets should come from local buyer phrasing, not a translation of your English set.

Where to find the language for your hints

The best hints aren't invented in a brainstorm. They're lifted from what buyers already say. In order of usefulness:

1. Sales-call notes and recordings. Listen to the first two minutes of each call, where the prospect explains why they're looking. That is the trigger, word for word.
2. Support tickets and onboarding emails. Look for the problem people had just before they found you.
3. Reviews, both yours and your competitors', on G2, Capterra, Trustpilot or Amazon. “We switched because…” sentences are ready-made switching hints.
4. Your Google Ads search-terms report. Long, question-shaped queries are compressed conversations. Expand them back out.
5. ChatGPT itself. Ask how a buyer in your category would describe their problem. Then check the output against the sources above, because a model's guesses are not your buyers' words.

Turning a Google Ads keyword list into context hints

A prompt you can paste into ChatGPT to get a first draft:

I sell [product] to [customer]. Our best customers usually come to us when [trigger].
Write 10 context hints for ChatGPT Ads. Each hint should be one or two sentences
describing a person in a specific situation (who + when + what they need), written in
the words a buyer would use. No demographics, no locations, no brand slogans.

50 worked examples by vertical

The fifty hints below follow the Audience–Intent–Topic framework. Each is written so you can paste it into an ad group, swap the product reference for your own, and have a usable starting hint within a minute. They are deliberately concrete because the auction rewards specificity.

B2B SaaS (5)

E-commerce and DTC (5)

Fintech (5)

Healthcare and life sciences (5)

These target business buyers of software such as practice managers and compliance officers. OpenAI doesn't show ads in conversations about health or mental health themselves, so a hint describing a patient's symptoms will never deliver.

Legal and professional services (5)

Education (5)

Local services (5)

Consumer apps (5)

Manufacturing and industrial (5)

Marketing and creative tools (5)

Pricing benchmarks — what $3-5 CPC and $60 CPM actually mean

OpenAI publishes two starting reference points. They recommend a starting CPC max bid of $3-5 USD per click and set the default CPM max bid at $60. These are not floors — they are starting suggestions inside a relevance-weighted auction.

In practice we see three patterns:

For CPM (Reach) campaigns the $60 default is generous. We have seen brand-awareness campaigns clear at $15-25 CPM when the hint is well-scoped to a small but exact audience. Treat the $60 as a ceiling, not a target.

Choosing between CPC, CPM and Conversions

• Use CPC (Clicks objective) when you have a measurable conversion event and you want to pay per outcome. Most lower-funnel demand-gen campaigns belong here.
• Use CPM (Reach objective) when you are introducing a category or building familiarity ahead of a future evaluation. Most brand campaigns belong here.
• Use the Conversions objective (oCPC) once the pixel and Conversions API are reporting cleanly. It still bills per click but optimises toward clicks likely to convert. See the conversion campaigns guide.
• You can run more than one objective for the same product. They live in different campaigns. For budget planning by business size, see how much ChatGPT ads cost.

The bulk upload schema

Ads Manager Beta supports bulk creation via CSV upload. The schema mirrors the object model: each row represents an object, and the rows are linked by name references. The high-level shape is:

campaign_name, objective, daily_budget, schedule_start, schedule_end
ad_group_name, parent_campaign, context_hints, bid_strategy, max_bid
ad_name, parent_ad_group, title, copy, landing_page, image_asset, advertiser_name, favicon

A few non-obvious points about working with the CSV:

• Context hints in the CSV are typically pipe- or semicolon-delimited inside a single cell. Verify the exact delimiter in the latest documented schema before importing.
• Ad group names must be unique within a campaign, and ad names unique within an ad group.
• If validation fails on a single row, the platform usually rejects the entire upload — keep your batches small until you trust your template.
• Use the OpenAI-published Bulk Upload Campaign Schema Checklist as your final pre-flight gate. We mirror its structure in the UTM Builder for downstream attribution consistency.

How to test context hints when Ads Manager reports nothing per hint

This is the question practitioners ask most, and the honest answer is that you can't measure a single hint. Impressions, clicks, spend and conversions are reported per ad group, not per hint. So the ad group is your test unit:

1. One theme per ad group. Put each situation you want to compare, such as “switching from a competitor” vs “first-time buyer”, in its own ad group with its own five to fifteen hints.
2. Hold everything else equal. Use the same bid, comparable budgets, the same ads and the same landing page, so the only difference is the hints.
3. Tag every ad group. Put the ad-group name in a URL parameter so revenue lands in your analytics against the hint theme that earned it. See dynamic URL parameters and UTMs.
4. Measure conversions, not clicks. Install the OpenAI pixel and the Conversions API before you start. Click-through rate tells you about the ad. Conversions tell you whether the hint found buyers. The setup is in ChatGPT Ads conversion tracking.
5. Wait for enough data. A week of a few dozen clicks per group is a direction, not a verdict. Keep the winning theme, rewrite the loser and start a new comparison.

Troubleshooting: my ad group isn't delivering

Work through these in order. The early checks are quick and rule out the most common causes:

1. Review status. Are the ad, the brand and the account approved? A brand-name change sends the account back to review and pauses serving.
2. Billing and budget. Is a payment method on file, and is the daily budget large enough for at least a few clicks at your bid?
3. Eligible audience. Do the campaign's countries, inclusion audiences and exclusions leave anyone to reach?
4. Bid. Is the max bid below what Ads Manager's bid guidance suggests for delivery? Raise it for a test, not for good.
5. Hint scope. Hints that are too narrow describe conversations that rarely happen. Hints that are too generic lose on relevance. Rewrite using the situation format above.
6. Category. Does the hint describe a health, mental-health or political conversation? Those never carry ads.
7. Alignment. Do the title, copy and landing page say the same thing as the hints? Mismatch lowers the relevance score.

The seven most common mistakes that kill ad-group performance

1. One generic hint per ad group. "Best CRM software." The auction treats you as a commodity bidder. Rewrite using the framework.
2. Hint and ad copy describe different things. The relevance score reads both. If the hint says "for healthcare startups" and the ad says "for everyone," the score is dragged down. Mirror your hint inside the ad copy. OpenAI's guidance points to headlines around 16 characters and descriptions around 32, so the hint's key phrase has to survive heavy compression. See creative best practices and anatomy of a ChatGPT ad.
3. Landing page mismatch. The landing page is one of the four named relevance inputs. Linking the hint "modern CRM" to a generic homepage instead of a vertical landing page is a relevance penalty.
4. No competitive context in the hint. "Switching from HubSpot" and "evaluating new CRMs" are different conversations. Include the competitor if your offer is a credible alternative.
5. Hint vocabulary the buyer never uses. If your hint contains your internal product category name and the buyer uses an industry term, you will miss the match. Write hints in the buyer's vocabulary, not yours.
6. Too few hints per ad group. Three hints describing slightly different phrasings of the same audience-intent-topic combination outperform one. Variants compound.
7. Mixing audiences in one ad group. "Founders and CMOs and developers" is three ad groups, not one. The mixed group loses to each focused competitor.

Context hints vs Google search keywords

For paid-search marketers crossing over, the mental model that helps most is this: treat each context hint like a SKAG (Single Keyword Ad Group) — except the keyword is now a paragraph. The discipline of writing one ad group per intent is the same. What changes is that the intent is now a fully-described state rather than a typed string.

For a focused, practical side-by-side of how the two models work — plus a translation table that maps your paid-search habits to their context-hints equivalents — see context hints vs keywords.

Context hints by business type

• E-commerce. Product feed campaigns generate ads from your catalog. Hints still steer which conversations those products appear in. Write them around the shopping situation (“gift for a dad who cycles”), not the SKU. See the ChatGPT product feed guide, bulk product feed campaigns and our e-commerce service.
• B2B SaaS. Long, specific evaluation conversations are where hints beat keywords most clearly. Build ad groups by switching trigger and company stage. See ChatGPT Ads for SaaS.
• Local and small businesses. Put the urgency and the job in the hint (“no heat tonight”) and leave the geography to location targeting. See ChatGPT ads for small business.

Where context hints are going

Three plausible directions in the next twelve months, based on patterns in adjacent platforms:

• Hint quality scoring shown in-product. Expect Ads Manager Beta to surface a relevance estimate per hint, similar to Google's Quality Score, so operators can iterate without launching.
• Audience extension across LLM surfaces. If Gemini, Copilot or other AI inventories open conversational ad buying, the same situation-style hint writing is likely to carry over, with each platform tuning its own relevance model. Perplexity has already wound its ads programme down. The state of play is in the AI ads platform landscape.
• Negative hints. Today the only exclusion tools are tighter hints and campaign-level audience exclusions. The ability to exclude a conversation type (“not for enterprise buyers,” “not for free-trial-only intent”) is the feature advertisers we talk to ask for most.

Frequently asked questions

Tarun Kapoor

Founder & CEO, Context Hints

Twelve years of media buying across GroupM, WPP, Ogilvy & Mather, and Neil Patel Digital — where he was directly responsible for $3.5M+ added to the bottom line of a $6.5M ARR agency. Has personally owned media for Nestlé, Sage, Qualcomm, Aetna, Weight Watchers, Chubb and Novotel. Now consults Fortune 500s and venture-backed startups as a fractional CMO and operative media buyer.