The Context Gap: Why General AI Fails at Niche Tasks (And How to Fix It)

In early 2025, a marketing director at a mid-sized SaaS company—call her Sarah—decided to try something new. She’d been hearing about ChatGPT for over a year. Everyone said it could write copy. She had a product launch coming up and a blank Google Doc that needed to become an email campaign by end of week.

She opened the interface and typed: Write marketing copy for a new analytics platform.

What came back was grammatically perfect. It had bullet points. It mentioned “data-driven insights” and “empowering teams.” It sounded like every SaaS website she’d ever seen.

She tried again, this time with more detail. Still generic. She gave up and wrote it herself.

Sarah’s problem wasn’t that the tool failed. It’s that she asked it to solve the wrong problem—and the tool, being helpful, gave her exactly what she asked for.

The average of everything is average

Here’s what happens when you ask a general-purpose language model to write marketing copy.

The model doesn’t “know” what good marketing copy is. It has seen millions of examples—landing pages, emails, ads, blog posts, pitch decks. It has learned the statistical patterns of how words tend to follow other words in documents labeled “marketing.” When you ask it to generate something, it samples from that distribution.

The problem is that most marketing copy in the training data is mediocre. It’s template-driven, jargon-heavy, interchangeable. The model has seen far more bad examples than good ones, because there’s far more bad copy in the world than good copy.

So when you ask for “marketing copy” without further constraint, you get the average of all marketing copy ever written.

And the average is, by definition, average.

The tool isn’t broken. You’ve just asked it to optimize for the wrong thing.

What constraints actually do

Here’s the same request, reframed three different ways.

Version 1 (what Sarah tried): Write marketing copy for a new analytics platform.

Version 2 (slightly better): Write a 150-word email to CFOs at Series B startups announcing a new analytics platform. Focus on cost savings and ease of integration.

Version 3 (specific): Write a 150-word email to CFOs at Series B startups who’ve tried building internal analytics dashboards and failed. Write in the tone of a peer who’s been there, not a vendor. Open with one concrete example of a decision that gets delayed when data lives in three different tools. No jargon. One CTA.

The outputs are not incrementally better. They’re different.

Version 1 produces a generic pitch. Version 2 produces something closer to usable, but still template-feeling. Version 3 produces something that sounds like it was written by someone who understands the problem—because you’ve told the model what problem it’s solving.

You’re not asking for marketing copy. You’re asking the model to write as though it understands who the reader is, what they’ve tried, and why they’re skeptical.

The constraints aren’t limiting the model. They’re relocating it within the space of possible outputs. Instead of sampling from “all marketing copy,” it’s sampling from “emails written by people who’ve worked at startups, addressed to finance leaders, in a peer-to-peer register, about a specific pain point.”

That’s a much smaller, much higher-quality distribution.

Why general models are bad at niche tasks

The issue isn’t unique to marketing copy. It shows up everywhere:

• Legal drafting. A general model can produce a contract that looks like a contract. It can’t produce one that reflects the specific risk posture of your industry, the jurisdictional nuances of your market, or the three clauses your GC has spent five years refining.

• Code reviews. A model can catch syntax errors and suggest refactors. It can’t tell you whether this architecture will scale with your team’s velocity, or whether you’re about to create a dependency that will haunt you in six months.

• Financial modeling. A model can build a DCF. It can’t tell you which assumptions your board will push back on, or how to structure the sensitivity analysis so the CFO trusts it.

The pattern is the same. The model has broad competence. The task requires narrow competence—inside a specific context, with specific constraints, optimized for a specific audience.

General-purpose tools are incredible at being generally useful. They’re bad at being specifically useful unless you do the work of making the task specific.

And most people don’t know how to do that, because they’ve never had to.

The real unlock isn’t better prompts—it’s better environments

Here’s where the story gets more interesting.

The reason “prompt engineering” became a job title in 2023 is that someone had to close the context gap manually. Every time. For every request.

But that’s not sustainable. If the path to useful AI is “learn to write 200-word prompts that include audience, tone, format, constraints, examples, and edge cases,” most people won’t do it. It’s too much work. The tool stops being a tool and starts being a puzzle.

The companies that are figuring this out aren’t teaching their employees to write better prompts. They’re building environments where the context is already embedded.

A few examples:

• Custom GPTs with system prompts. A marketing team builds a GPT with a pre-loaded brief: target personas, brand voice, competitive positioning, approved messaging. Now when someone asks for copy, the model isn’t starting from zero. It’s starting from the company’s specific context.

• Structured workflows. A legal tech company builds a tool that asks five questions before generating a contract: industry, deal size, jurisdiction, risk tolerance, template preference. The user never writes a prompt. The tool constructs one behind the scenes.

• Domain-specific models. A healthcare startup fine-tunes a model on clinical documentation from their specific EHR system. It doesn’t need to be told what “patient presents with” means in their workflow—it already knows.

What these have in common: the user doesn’t supply the context. The environment does.

That’s the shift. The first wave of AI tools was general-purpose models made available to everyone. The second wave is specialized environments built around those models, where the constraints that produce quality are built into the system, not typed into a text box every time.

What this means for how work gets done

If you’re building tools—or deciding which ones to adopt—the implication is straightforward.

The tools that win won’t be the ones that do everything. They’ll be the ones that do one thing with enough context baked in that the output is immediately usable.

A general-purpose model is a starting point. A useful tool is a general-purpose model wrapped in the constraints that make it good at a specific job.

This is already happening:

• GitHub Copilot doesn’t just autocomplete code. It learns your codebase, your patterns, your team’s conventions. The suggestions get better the more context it has.

• Jasper and Copy.ai started as wrappers around GPT-3. The ones that survived added templates, brand kits, audience profiles—layers of constraint that turned “generate marketing copy” into “generate our marketing copy.”

• Harvey and CoCounsel aren’t just LLMs for lawyers. They’re trained on legal precedent, structured around legal workflows, and designed to output in formats lawyers actually use.

• Axyen.AI takes a similar approach—building specialized environments where context, tone, and format constraints are embedded into workflows, so users get usable output without having to engineer prompts from scratch every time.

The pattern repeats. The tool that’s marginally better at everything loses to the tool that’s significantly better at one thing—because “one thing” is what people are actually trying to do.

General competence is table stakes. Specific competence is the product.

The gap isn’t going away

Here’s the part that matters as we move through 2025.

The context gap doesn’t disappear as models get better. Smarter models are still general-purpose models. They’ll be better at more tasks, but they’ll still produce generic output when you ask them generic questions.

The gap closes when someone—either the user or the tool builder—does the work of making the task specific.

Right now, that work is mostly manual. People are learning to write better prompts. Teams are building custom GPTs. Companies are fine-tuning models.

But the real shift happens when that work becomes infrastructure. When the constraints that produce quality are embedded in the tools themselves. When “write marketing copy” becomes “write marketing copy for this company, in this voice, to this audience, in this format”—and the user never has to specify any of that because the system already knows.

That’s what the next generation of tools looks like. Not smarter AI. Smarter environments around AI.

Sarah eventually figured it out. She didn’t get better at prompting ChatGPT. She built a template—a structured doc with audience profiles, messaging pillars, tone guidelines, and example copy. Now when she needs something written, she fills in three fields and pastes the whole thing into the model.

It works. The output is usable. Sometimes it’s good.

But she’s also watching the tools that are starting to do this for her. The ones where she doesn’t have to build the template. Where the context is already there.