What does the ContextHint plugin tutorial cover?

It follows a ChatGPT Ads planning workflow from connection and product context through similar-advertiser research, creative and prompt review, market patterns, ad-group hypotheses, and context-hint evaluation.

Can I follow the tutorial in ChatGPT, Claude, or Codex?

The research workflow applies across supported AI clients, but their connection interfaces can differ. Use the current ContextHint setup guide for the client you are connecting today.

Does the plugin write the context hint?

The connected assistant writes the candidate from the brief and evidence. ContextHint prepares the evidence and evaluates supported candidates against relevant and contrastive prompts.

Are competitor ad groups and context hints directly observed?

No. Captured creatives and associated prompts are observed. Competitor audience, intent, ad-group, and context-hint patterns are inferences based on those records and should be presented as such.

Can ContextHint publish the campaign shown in the tutorial?

No. The plugin supports research, planning, and evaluation. It does not access a private Ads Manager account, publish campaigns, change bids, or edit live ad groups.

Why might the connection screens look different from the video?

The tutorial records a product workflow at a point in time. AI client labels and connection screens can change, so the current setup page remains the source for connection steps.

How to Use the ContextHint ChatGPT Ads Plugin

Direct answer

How do you use the ContextHint plugin for ChatGPT Ads?

Connect ContextHint to the AI client you already use, describe the product and buyer, then ask the assistant to research similar advertisers and inspect captured creatives and prompts. Use those records to form ad-group hypotheses. The assistant writes each context-hint candidate, ContextHint evaluates it, and you review the evidence before making a campaign decision.

Full walkthrough

Watch the 11 minute plugin tutorial

The video begins with connection, then follows one product through advertiser research, creative review, and targeting decisions. Connection screens change, so use the current setup guide when the recorded interface differs.

Before you follow the walkthrough

Bring a short but specific product brief. Include the product, intended buyer, buying situation, and meaningful difference. The plugin can retrieve evidence, but it cannot repair an ambiguous brief by guessing which market matters.

Product

What does it help someone do?

Name the job and the outcome, not a list of features.

Buyer

Who has the problem?

Describe the role, company context, and moment of need.

Difference

Why would they choose it?

Give the research a reason to separate close rivals from adjacent ones.

The tutorial, chapter by chapter

Each step moves further from the source records. Keep those records visible as the conversation moves from retrieval into interpretation and recommendation.

01

Connect ContextHint

Add the private ContextHint connection to the AI client you use, then confirm that the research capabilities are available in the conversation. Use the current setup guide for live interface instructions.

02

Give the assistant a useful brief

Explain what the product does, who buys it, and why it differs. A specific brief gives advertiser discovery a stronger basis than a short category label.

03

Find and check similar advertisers

Ask for companies whose captured ChatGPT ads are close to the brief. Read the match explanation and reject adjacent companies that do not share the same buyer or use case.

04

Inspect creatives and prompts

Open the captured creative and the prompts observed with it. These records can show what appeared together in the sample, but they do not prove why an ad was served or how it performed.

05

Study niche and intent patterns

Compare the conversations, audiences, and message themes that recur across the evidence. If a niche mixes different buyers, narrow the research around the product brief.

06

Turn patterns into ad-group hypotheses

Let the assistant propose focused buyer conversations supported by the research. Treat them as recommended structures, not private ad groups recovered from another advertiser.

07

Write and evaluate context hints

The assistant writes a structured candidate. ContextHint evaluates it against relevant and contrastive prompts, then returns scores and revision signals for review.

08

Open the evidence before deciding

Follow the advertiser, creative, and library links behind the answer. Keep uncertain conclusions visible and validate campaign performance in your own Ads Manager data.

The assistant and the plugin have different jobs

The clearest workflow says where an answer came from. This matters most when an observed record becomes an inferred pattern and then a recommendation.

Assistant

Writes and explains

It uses your brief, asks questions, interprets retrieved evidence, and writes proposed structures and creative.

ContextHint

Retrieves and evaluates

It returns relevant records, market patterns, source links, and evaluation signals for compatible context-hint candidates.

Advertiser

Reviews and decides

You reject weak comparisons, choose what fits the campaign, and measure actual results in your own systems.

Evidence boundary

Research evidence is not private account data

Captured creatives, associated prompts, advertiser appearances, and source timestamps are observations from the available sample. Audience, intent, ad-group, competitor, and context-hint patterns are inferences. ContextHint does not establish a competitor's settings, spend, impressions, clicks, conversions, or campaign performance.

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