Messaging prompts

AI competitor messaging teardown prompt

Break down competitor messaging by promise, proof, audience, objections, and repeated phrases.

This is a working analyst brief. Sources go in. Patterns, risks, and decisions come out.

Use this prompt

You are a messaging analyst.

Teardown {{competitor}}'s messaging for {{market}}.

Source material:
{{sources}}

My company:
{{my_company}}

Return:
1. The main promise.
2. The audience language they use.
3. The phrases they repeat.
4. The proof they use.
5. The objections they address.
6. The objections they ignore.
7. The emotional angle.
8. What we can learn without copying their voice.
9. A sharper messaging brief for us.

Rules:
- Quote short phrases only from the supplied sources.
- Do not invent customer voice.
- Keep recommendations practical.

Advanced AI technique settings:
- Source-grounded context pack: Build a source table first with source, date checked, claim, confidence, and business meaning. Use only that table for the final recommendations.
- Few-shot calibration: Add one or two examples of a good finding and a bad finding before the source pack, then follow that standard in the output.
- Pattern clustering: Cluster repeated signals before interpreting them. Label one-off examples as one-offs and do not treat them as strategy.
- Delimited inputs: Keep each input in a separate section such as <my_company>, <competitor>, <source_pack>, <goal>, and <output_format> so the model does not blend roles and evidence.
- Verification loop: After the first draft, run a verification pass that lists unsupported claims, stale details, missing sources, and recommendations to downgrade or remove.

Copy the prompt. Fill the variables. Then check the output for real.

Advanced AI techniques

Use these techniques for this prompt

These are selected for this specific competitor research job. Use the prompt-ready instruction when it helps, and skip it when the condition does not fit.

Source grounding

Source-grounded context pack

Use when: Use when the answer depends on competitor pages, screenshots, ads, pricing, SEO exports, or reviews.

Prompt move: Build a source table first with source, date checked, claim, confidence, and business meaning. Use only that table for the final recommendations.

Skip when: Skip only for brainstorming with no factual claims.

Calibration

Few-shot calibration

Use when: Use when tone, scoring, or finding quality matters more than generic completeness.

Prompt move: Add one or two examples of a good finding and a bad finding before the source pack, then follow that standard in the output.

Skip when: Skip when you do not have a reliable example to imitate.

Pattern analysis

Pattern clustering

Use when: Use for batches of ads, emails, social posts, reviews, SEO pages, or competitor claims.

Prompt move: Cluster repeated signals before interpreting them. Label one-off examples as one-offs and do not treat them as strategy.

Skip when: Skip for a single landing page or one pricing table.

Prompt structure

Delimited inputs

Use when: Use when mixing company context, competitor evidence, goals, examples, and output requirements.

Prompt move: Keep each input in a separate section such as <my_company>, <competitor>, <source_pack>, <goal>, and <output_format> so the model does not blend roles and evidence.

Skip when: Skip for very short single-source prompts.

Verification workflow

Verification loop

Use when: Use before sharing research with a client, team, sales deck, ad brief, or website backlog.

Prompt move: After the first draft, run a verification pass that lists unsupported claims, stale details, missing sources, and recommendations to downgrade or remove.

Skip when: Skip only for private rough notes.

Replace placeholders

Replace these variables before running the prompt

Variable	Meaning	Type	Example
`{{my_company}}`	Your company, product, or brand	string	Northstar CRM
`{{competitor}}`	The competitor you want to analyze	string	Acme CRM
`{{market}}`	The category or market context	string	B2B CRM for agencies
`{{sources}}`	URLs, screenshots, notes, exports, or pasted copy	list	Homepage URL, pricing URL, ad screenshots

Expected shape

Compare a filled input with a realistic output shape

The output below is fictional. It shows the shape you are looking for, not a real competitor result.

Example input

my_company = PixelStock
competitor = AssetFlow
market = digital asset management for ecommerce teams
sources = homepage copy, two ad captions, product page snippet

Fictional example output

Fictional example output:

Main promise:
AssetFlow promises fewer lost assets and faster campaign launches.

Repeated phrases:
- "one source of truth"
- "ship campaigns faster"
- "built for ecommerce teams"

Ignored objection:
- The page does not explain migration effort.

Messaging brief for us:
Lead with "find every product asset before launch week" and support it with workflow proof.

Prompt logic

Why this prompt works

It looks for repeated language, not just the hero headline.
It keeps source quotes short and grounded.
It turns teardown notes into a useful messaging brief.

Mistakes to avoid

Asking the AI to analyze a competitor with no sources.

Paste the page copy, ad screenshots, pricing table, SEO notes, or transcript first.

Treating the output as research truth.

Use it as a source-backed brief: keep strong evidence, downgrade weak evidence, and decide what deserves action.

Asking for generic strategy advice.

Ask for observations, risks, and next actions tied to the evidence.

Verification checklist

Every factual claim has a source or is marked as unverified.
Pricing, dates, and product claims were checked on the original source.
The output separates observation from interpretation.
The output gives actions you can reject, edit, or test.
Nothing is treated as final just because an AI tool wrote it.

Use the output safely

What you should do next

Collect real copy before running the prompt.
Pull the repeated phrases into a short swipe file.
Rewrite your messaging brief with proof, not adjectives.