By Maria Dykstra | Founder, TreDigital | Creator, Founder Visibility Engine™
Published: October 20, 2025 | 12-minute read

Why Founders Are Losing the Visibility Game

Search “best ChatGPT prompt” on LinkedIn, and you’ll see an endless scroll of confident advice: one-line hacks promising viral posts, flawless emails, or instant content calendars.

In August 2025, I tested 47 viral prompt templates. Each produced a single solid result before sliding into sameness. Tone, rhythm, even word choice blended into a predictable pattern of “professional AI voice.”

So why most of them did not work, at least, not more than once.

The reason is simple. Prompts are instructions, not intelligence. They tell AI what to do, but not how you think. It’s like hiring a talented intern who can write anything, but knows nothing about your market, product, or story.

Prompt Engineering = Input Design

Goal: Make AI give you a good answer.

You’re focused on crafting the perfect question or command to get a specific output.

Think of it like talking to a junior copywriter:
you give them clear instructions, tone, format, and examples so they produce what you want.

Example:
“Write a LinkedIn post in my founder voice about Walmart’s OpenAI integration. Highlight how it signals the next phase of conversational commerce.”

That’s prompt engineering.
It’s tactical. You’re shaping the request.

2. Context Engineering = System Design

Goal: Make AI understand your world before it even writes.

It’s not just the prompt — it’s the entire information environment the AI sees and reasons from.

You feed it:

• your brand voice guides
• previous posts or frameworks
• customer personas
• company positioning
• even metadata like hashtags, schema, or tone rules

When you do this, you’re building a context layer — a sort of memory palace that makes every new prompt smarter and more consistent.

So instead of just asking for “a blog post about AI visibility,”
you give AI your POV, your strategy, and your structure before you ever prompt.

That’s context engineering.
It’s strategic. You’re shaping the thinking environment.

AI visibility depends on how AI understands your authority.

• Prompt engineering helps you create content.
• Context engineering helps AI recognize your expertise.

When you train your content systems (and later, AI models like ChatGPT, Gemini, or Perplexity) on consistent context — your frameworks, tone, expertise — your whole online footprint becomes coherent and verifiable.

That coherence is what AI search rewards.

Think of it this way:

Prompt engineering gets you noticed by humans.
Context engineering gets you remembered by machines.

His next post hit 127,000 impressions. Two inbound leads closed in 30 days.

Nothing about his product changed. Only his context did.

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The Hidden Engine Behind AI Search

AI search engines don’t rank creativity or personality. They rank three specific signals:

1. Structure. Clear hierarchy beats clever prose every time.

2. Consistency. Repeated patterns and terms signal deep expertise.

3. Proof. Data, examples, and citations verify your claims.

I did not invent this. It is supported in all of the documentation:

• Google Search Central confirms that both Google AI Overviews and Google AI Mode “surface relevant links to help people find reliable information quickly.”
• Perplexity CEO Aravind Srinivas told CNBC that “our goal is to synthesize information from several sources, aggregate it into a good summary, and give users more guidance.”
• On the Lex Fridman Podcast (#434), he added, “all our answers are backed by sources… with appropriate footnotes to every sentence.”
• OpenAI’s Help Center explains: “Be specific, descriptive, and as detailed as possible about the desired context, outcome, length, format, and style.”
• A Nature study (2023) found that “mitigation prompts” (or brief instructions to verify facts) cut hallucinations by nearly 20 percentage points.

In short: the better your context, the smarter AI becomes at understanding you.

The rise of Generative Engine Optimization (GEO) builds on this principle. Instead of optimizing web pages for keywords, GEO optimizes structured content so AI search engines (ChatGPT, Gemini, Perplexity) can identify credible experts faster.

If SEO told Google what you said, GEO tells AI why it matters.

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What AI Actually Rewards

AI systems like ChatGPT, Gemini, and Perplexity don’t “rank” you based on how often you post. They surface voices that:

• Stay on one core topic
• Use consistent language, frameworks, and examples
• Show evidence of expertise over time

Think of it like teaching a student who only remembers repetition.
If you say “AI strategy for SaaS founders” fifty different ways, the AI doesn’t link them.
If you repeat the same phrasing and structure, it learns that you = authority on that phrase.

When I compared two SaaS founders in a September 2025 visibility audit, their results looked like this:

Category	Founder A	Founder B
Focus	Random topics	Consistent theme: AI adoption for mid-market SaaS
Posting volume	40 posts	40 posts
Appearance in AI search (ChatGPT, Perplexity)	0 / 10 relevant queries	8 / 10 relevant queries
AI-generated summaries citing them	1 (misquote)	6 (accurate)

Same effort. Different results.

Founder B’s advantage wasn’t posting frequency, it was structured consistency. Repeating frameworks, terminology, and proof points teaches AI that you’re a reliable authority on a single topic.

This matches findings from Superlines (2025), analyzing 1.5M AI citations: “Chunk-level factual content increases the likelihood of consistent attribution.”

Volume creates noise. Structure creates signal.

This means:

• You don’t need to post more.
• You need to post the same ideas, refined and indexed in multiple forms (blog, quote, case study, Q&A).

That’s what our Founder Visibility Engine does. It builds this “AI-trainable” content layer from your voice.

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Context Engineering vs. Prompting

Most founders still rely on prompting or tactical instructions for single tasks. Context engineering replaces that with strategic architecture.

Prompting says: “Write a LinkedIn post about AI tools.”
Context engineering says:

“I’m Maria Dykstra. I help B2B SaaS founders with 10–50 employees adopt agentic AI without replacing their teams or blowing their budgets. My audience: CEOs who understand AI’s potential but fear implementation chaos. My tone: direct, data-driven, skeptical of hype. I cite real studies and avoid jargon. I use frameworks with names people remember. My proof points: Microsoft ad systems ($2B revenue), TreDigital (13 years), helping bring agentic AI to market for Exactly AI Solutions. Now write a LinkedIn post about AI adoption barriers for mid-market companies.”

The first line gives AI a job.
The second gives AI your brain.

Think of prompting as ordering from a restaurant menu. Context engineering teaches the chef your allergies, flavor preferences, and signature dishes. Every time you “order,” the output improves because the system knows you.

That compounding improvement—context learning—is how founders scale voice without scaling workload.

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The Two-Part Context System

Building your own context system takes about two hours and requires no technical setup.

It’s built around two documents:

1. The Context File

Your personal operating manual. It captures how you think, write, and decide.

Include six sections:

1. Voice Samples – 3-5 paragraphs where you sounded most like yourself (from posts, talks, or interviews).
2. Positioning – Three lines defining who you serve, what transformation you deliver, and what fear you remove.
3. Frameworks – Name and explain your core mental models (e.g., Clarity System: Context > Commands).
4. Proof Points – Credentials, case studies, measurable outcomes.
5. Audience Intelligence – Real quotes of client pain points, goals, and objections.
6. Content Standards – Writing rules (active voice, citation norms, banned buzzwords, preferred phrasing).

2. The Strategy Document

Your business compass. It connects your context to revenue priorities.

Include:

• Positioning statement (audience, outcome, obstacle).
• Audience segments (primary clients vs. secondary influencers).
• Key messages (the three to five topics you want to own).
• Proof points (media mentions, partnerships, performance data).
• Content priorities (which subjects directly support current business goals).

Together, these two files create your “founder fingerprint.” Once saved in ChatGPT or Claude as custom instructions, they transform AI from assistant to proxy thinker.

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Why Uploading Old Posts to ChatGPT is NOT Context Engineering

Uploading old posts to an AI tool (like ChatGPT’s memory or a custom GPT) doesn’t automatically “teach” it your expertise.

Because that content was written for human engagement, not machine understanding.

Old posts often:

• Lack clear business outcomes.
• Mix multiple themes or tones.
• Miss structure that connects ideas to your offers.

AI can imitate your voice, but not your intent, unless you give it context.

Voice ≠ Strategy

AI copies surface patterns: phrasing, rhythm, tone.
It doesn’t know which posts actually converted, or which ideas built trust.
So if you just feed it all your past writing, it can’t tell what matters to your business.

Think of it like teaching a student by giving them your entire notebook, unlabelled.
They’ll learn your handwriting, not your lessons.

Why engagement ≠ inbound

Founders often chase likes, thinking engagement equals growth.
But high engagement posts without strategic framing train AI (and audiences) to see you as “interesting,” not authoritative.

When you clarify your core context — your frameworks, value props, and proof points — every post signals what you actually sell.

The real fix: Context Systems

A context system maps your voice to your business goals:

• Which ideas lead to leads.
• Which frameworks prove authority.
• Which proof points convert attention into trust.

Then, when AI helps you generate or repurpose posts, it works inside that system, keeping your tone and outcomes aligned.

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From Content Chaos to Context Clarity: A Case Study

A fintech founder built his context file in July 2025. Before, 60% of his AI drafts missed the mark—wrong tone, irrelevant examples, random topics.

After documenting his positioning and key frameworks, 9 out of 10 drafts aligned with his pipeline messaging. Within two months, he closed two $150K enterprise deals from LinkedIn inbound.

The difference wasn’t creativity. It was alignment.

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How to Write Context-First Prompts and AI-Trainable Loops.

Every prompt should start with your context file, then add the task.

Old prompt:
“Write a LinkedIn post about AI adoption barriers.”

Context-first prompt:
“Context: [paste context file]
Task: Write a 300-word LinkedIn post about the top three AI adoption barriers for mid-market SaaS companies. Focus on implementation, not tool selection. Use one framework and one data-backed example. End with a question that invites comments.”

This single structure works for every output, blog, email, webinar, or script. Build once. Reuse forever.

Let me show you how to design your own “AI-trainable” content loop. We’ll go step by step and I’ll ask you questions as we go so you can build your own version.

Step 1: Pick your core idea cluster

AI learns through repetition and semantic proximity.
That means you need one anchor topic — your Visibility Core — that all your posts, blogs, and interviews orbit around.

For example:

• “AI adoption for mid-market SaaS”
• “Agentic AI for local experts”
• “Trust signals in AI-driven search”

Question for you:
What’s the single idea you want AI to associate your name with?

Step 2: Define 3 recurring frameworks

These are your repeatable structures — the things you say again and again in slightly different contexts.
Think of them as your “proof points.”

Example for “AI adoption for mid-market SaaS”:

1. The Readiness Gap – most teams buy AI before they’re ready.
2. The System Bottleneck – AI tools fail when data isn’t structured.
3. The Trust Layer – visibility depends on verifiable expertise.

Each time you post, you echo one of these frameworks. That’s what teaches AI to connect your name to real substance.

Next question:
Can you name 2–3 frameworks or repeatable ideas that keep showing up in your content?

Step 3: Signal Structure

Think of this as your “SEO for Generative AI.”

AI engines scan for patterns of authority — not just backlinks or keywords, but consistent evidence that you’re a trusted source.

Here’s how to teach them to recognize and cite you:

1. Consistent Terminology

Use your key phrases exactly the same way across all assets.
If your framework is “AI Visibility Score,” don’t sometimes call it “AI Credibility Index.”
AI sees those as two separate ideas and splits your authority.

Tip: Create a 1-page “Language Ledger” of the 10–15 terms you’ll repeat everywhere.

2. Chunk-Level Formatting

LLMs read in chunks (small paragraph-sized meaning blocks).
So:

• Use sub-headings and bullet points (AI can identify topic shifts).
• Make each paragraph a self-contained mini-idea.
• Include your name or company near key ideas occasionally (“As Maria Dykstra explains in the Founder Visibility Engine…”).

That’s how you get cited accurately.

3. Proof-Rich Content

AI trusts measurable or factual anchors — numbers, examples, dates, frameworks.
E.g., “In our 2025 audit of 200 SaaS founders…” beats “Many founders struggle with consistency.”

Each concrete element becomes a retrieval hook.

4. Structured References

Link to your owned sources: blog posts, case studies, interviews.
When AI sees repeated cross-links to your site or Substack, it recognizes a content graph and attributes ideas to you.

5. External Validation

Get your ideas cited on other indexed sources (Medium, podcasts with transcripts, reputable sites).
AI models are trained on these open-web signals — they’ll reinforce your authority loop.

If we sum it up in one line:

Repetition builds memory. Structure builds recognition.

Step 4: The Visibility Loop

Think of this as the flywheel that turns your ideas into long-term discoverability.

1. Create → Standardize → Distribute

Every asset passes through the same path:

Stage	What you do	Why it matters for AI
Create	Capture insights via interview, note, or Loom	Keeps your raw voice authentic
Standardize	Apply the same headers, phrasing, and frameworks	Builds pattern memory for AI
Distribute	Publish to 3 core surfaces — LinkedIn, owned site, and one syndication outlet (Medium/Substack/YouTube transcript)	Expands your semantic footprint

2. Repurpose with Purpose

Each original post becomes:

• 1 LinkedIn insight post
• 1 Q&A snippet for ChatGPT-style retrieval
• 1 blog or case study with schema markup
• 1 micro-video or quote card

AI reads these as consistent signals from multiple modalities, reinforcing your expertise graph.

3. Index Your Proof

Add light structure:

• Use FAQ or How-To schema on blogs.
• Include your name, title, and niche in meta descriptions.
• Keep internal links between posts that share the same framework.

This lets AI search crawlers connect your ideas together.

4. Measure & Reinforce

Every month, run a quick “AI Search Audit”:
Search your name + your topic in ChatGPT, Perplexity, and Gemini.
Note what shows up, what’s missing, and what’s misattributed.
Then rebuild content to correct or reinforce the pattern.

5. Automate the Cycle

Once you’ve built ~30 core assets, you can automate re-posting and refreshing:

• Rotate one core theme each week.
• Layer in new proof points quarterly.
• Keep voice and framework constant.

Over time, this loop compounds visibility just like SEO once did.

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How to Optimize Content for AI Discovery

Optimizing for AI Discovery is nothing more than training an AI engine to recognize you. I’ll explain each principle and then give you a question to check your understanding.

1. Question-Based Headers

AI search is modeled after queries, not titles.
When you write subheads like “How Does Context Engineering Work?” or “What Are the Benefits of AI Visibility?”, you create direct question-answer pairs.
That makes your post retrievable by AI systems and featured in summaries.

Think of it this way:
Each subhead is a potential “prompt.” You’re pre-answering questions people (and machines) will ask later.

👉 Your turn:
What’s one question-style header you could use for your main topic?

2. Internal Linking

AI engines follow internal links the same way Google crawlers do — it helps them map your expertise.
If you link every post to three to five related resources with descriptive text (“See our guide to AI adoption frameworks”), AI sees your work as a connected knowledge graph, not isolated pieces.

Tip: Never use “click here.” Always describe what you’re linking to.

👉 Question:
Do you already have a few cornerstone articles you could link together?

3. Consistent Terminology

Repetition = Recognition.
If you keep swapping between “AI adoption,” “AI integration,” and “AI enablement,” AI splits your meaning across multiple nodes.
Pick one phrasing per core concept — and stick to it everywhere (posts, bios, podcasts, and schema).

👉 Mini task:
Write down 5–10 terms you’ll always use exactly the same way. That becomes your “AI vocabulary.”

4. Schema Markup

This is invisible code that tells AI what your content is:

• Article schema helps with general indexing.
• Person schema connects your name and credentials.
• FAQPage schema boosts retrievability of question-answer sections.

If you use WordPress, Yoast SEO or Rank Math can generate these automatically. Otherwise, Schema.org has free templates.

Key idea: Schema = structure. It’s how you whisper to machines, “This is expert content.”

👉 Question:
Have you ever added schema before, or would you like me to show you a starter example?

5. Update Cornerstone Content Quarterly

AI models notice freshness.
Even small updates — adding new stats, references, or formatting tweaks — trigger re-crawling and signal that your expertise is alive and maintained.
That’s how you stay relevant in AI Overviews and ChatGPT summaries.

👉 Reflection:
Which one or two pieces of your content deserve quarterly updates?

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Proof: Real Outcomes from Context-Driven Systems

One strategy consultant built her context file in a single afternoon.

Before: 30–40 minutes editing every draft.
After: 5 minutes of fact-checking per piece.

She tripled her monthly publishing output while keeping the same quality. Newsletter growth jumped 47% in a month.

That’s the compounding effect of context engineering—it’s not faster writing, it’s smarter iteration.

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Why Context Builds AI Trust

Let’s now look at why all your structured effort (headers, schema, consistency) actually works in AI discovery.

I will break this down like we’re reverse-engineering how an AI decides, “Whose voice can I trust enough to quote?”

1. Depth Recognition

AI rewards depth over range. When you keep repeating a core framework (for example, The Founder Visibility Engine™) across different formats (blog, Q&A, podcast transcript), the system detects pattern density.

Think of it like digital muscle memory:
Each repetition strengthens your “ownership signal.”
The more you repeat, the easier it becomes for the model to associate that concept with you.

Exercise for you:
What are two or three phrases or frameworks you want AI to permanently associate with your name?
(Examples: “Generative Engine Optimization,” “AI Visibility Audit,” “Agentic Marketing System.”)

2. Source Confidence

AI models weigh credibility cues the same way humans do. Citations, outbound links, and schema markup give your ideas structure that machines can verify.

When your blog post links to data, references studies, and includes Article or FAQPage schema, it tells the AI:

“This is factual, contextual, and maintained.”

That confidence increases the likelihood that you’ll be cited in summaries or used as a retrieval source.

3. Entity Attribution

This is the “who wrote it” layer. Clear author bios, consistent name formatting (“Maria Dykstra | Founder, TreDigital”), and linked profiles build a stable identity graph.

If your name, title, and domain appear together across multiple posts, AI learns to separate you from other people with similar names and attribute ideas correctly.

That’s what turns “some founder said this” into “According to Maria Dykstra’s Founder Visibility Engine…”

4. The Research Connection

Two 2025 findings support this shift:

• Ding et al. (arXiv): Trust in AI answers jumped even with random citations.
  → The presence of citations itself builds perceived reliability.
• NinePeaks study: Expert-written pages are 3.2× more likely to appear in AI Overviews.
  → Structured expertise signals outperform generic SEO content.

5. The Core Lesson

Consistency isn’t vanity. It’s discoverability.

When your frameworks, citations, and author identity align, AI starts to treat your content not as “posts,” but as reliable data. That’s how you become quotable, by machines and by people.

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Branding vs. Context: A Founder’s Lens

Branding is for humans

Your brand is emotional.
It’s the story, tone, visuals, and values that make people trust and remember you.
It answers:

“What do I want people to feel when they see my name?”

It lives in:

• Visual identity (logo, colors)
• Messaging tone (“smart but approachable”)
• Testimonials and social proof
• Taglines and mission statements

That’s how humans decide whether you’re credible, likable, or aspirational.

Context is for machines

Context is how AI interprets and represents you when you’re not present to explain yourself.
It’s mechanical, not emotional — it’s how systems label you in their internal knowledge graphs.

It answers:

“What do I want AI to say about me when someone asks who I am?”

It lives in:

• Schema markup (Person, Organization, Article)
• Structured bios with consistent titles
• Terminology and linguistic patterns
• Consistent citations and link relationships

That’s how ChatGPT, Perplexity, or Gemini decide whose voice to surface.

Human Branding	Machine Context
Vision and values	Data and definitions
Emotional story	Structured schema
Tagline	Positioning statement
Tone of voice	Linguistic rules
Social proof	Citation consistency

Your brand is how people feel about you. Your context is how AI interprets you when you’re not in the room.

Humans buy from trust.
Machines recommend based on structure.

If you only build brand, people may love you, but AI can’t find or cite you.
If you only build context, machines may know you, but humans won’t care.

The sweet spot is when your emotional story is wrapped in structured clarity.

When you combine context engineering with AI search optimization, you unlock three levels of visibility:

1. Human recognition – Your audience resonates because your content sounds like you.
2. Machine recognition – AI cites and recommends you accurately.
3. Market recognition – Consistent visibility compounds into authority, partnerships, and inbound leads.

This is the essence of Generative Engine Optimization (GEO)—not gaming algorithms, but teaching them who you are

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The 60-Second Visibility Test

Try this right now:

1. Open ChatGPT or Perplexity.
2. Type: [Your Name] + [Your Expertise Area].
3. Read the summary.

If the description is inaccurate—or nonexistent—AI hasn’t learned your context.

Then search your top three competitors. If they appear while you don’t, their content is structured better.

Fix that once, and you’ll see compounding gains for years.

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Key Takeaways

• Prompts create tasks. Context creates presence.
• AI rewards structure, not volume.
• Your content is training data—make it accurate.
• Visibility isn’t viral; it’s cumulative.
• Two hours of documentation can replace 40 hours of editing.

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Build Your Context System This Week

I’ve helped more than fifty founders turn generic AI outputs into authority engines using this exact framework.

If your content sounds polished but forgettable—if ChatGPT still misrepresents your expertise—let’s fix that.

Book a 15-Minute Context Strategy Call

We’ll audit your visibility, identify context gaps, and build a system that scales your voice—without adding hours to your calendar.

Availability: four founder slots remaining for October 2025.

Schedule your call →

FAQs: Context Engineering & AI Visibility

1) What is context engineering?
Teaching AI your thinking, voice, and proof—so outputs align with your strategy, not a generic “AI voice.”

2) How is it different from prompt engineering?
Prompts give tasks. Context gives memory. Prompts shape a single answer; context shapes every answer after.

3) Why does context matter for AI visibility?
AI surfaces sources it can verify. Consistent frameworks, terminology, and proof build machine-recognizable authority.

4) What goes in a Context File?
Frameworks, positioning, voice samples, proof points, audience insights, and content standards—organized on one page.

5) Will uploading old posts into ChatGPT work?
Not by itself. Those posts weren’t structured for machines. You need repeatable terms, chunked formatting, and clear proof.

6) How does this help with ChatGPT, Gemini, and Perplexity?
They read patterns. Context makes your patterns obvious—so you show up in summaries, citations, and follow-up questions.

7) What is Generative Engine Optimization (GEO)?
GEO structures your content so generative engines identify, attribute, and reuse your expertise across answers.

8) Do I need to post more to win?
No. You need to post the same core ideas with consistent language and proof—across formats—so AI learns you.

9) How do I measure if it’s working?
Run a monthly AI Search Audit: your name + topic in ChatGPT, Perplexity, Gemini. Track accuracy, citations, and gaps.

10) Common mistakes to avoid?
Mixing terminology, burying proof, inconsistent author bios, no schema, and starting new chats without pasting your context.

11) How long does a usable Context File take?
About 90 minutes for a solid v1. It will cut editing time and improve on-brand accuracy immediately.

12) First step if I’m busy?
Pick one core topic, define 2–3 frameworks, paste them + two voice samples into your next AI chat, then prompt from there.