What Makes Content Actually Work (And How to Write It)

The fundamental mistake most writers make is thinking about content from their own perspective. They ask what they want to say, what points they want to make, what structure feels natural to them.

But the reader doesn't care what you want to say. The reader has a problem. They typed a query into Google because they're trying to solve that problem, and your job is to solve it faster and more clearly than anyone else on the first page of results.

This sounds obvious, but look at most blog posts and you'll see writers who have completely forgotten it. They start with throat-clearing paragraphs about "the evolving landscape of technology" or "in today's competitive business environment."

They include sections that exist only to hit a word count. They hedge every opinion until the writing says nothing at all. These posts might rank through sheer domain authority, but they won't convert, because the reader can tell within seconds that the writer doesn't respect their time.

The content that works operates on a different principle entirely. It starts from the reader's frustration and works backward to the writing.

When someone searches "Lokalise alternatives," they're not looking for a neutral tour of the localization tool landscape. They're frustrated with Lokalise. Something specific isn't working for them. Maybe the pricing scaled faster than they expected when they added team members. Maybe the interface is too complex for their non-technical marketing team. Maybe the API doesn't integrate cleanly with their CI/CD pipeline and they're tired of manual workarounds.

Whatever the specific frustration, they've already tried one solution and found it lacking. They don't need to be educated about what localization is or why it matters. They don't need statistics about the global language services market. They need someone to acknowledge their frustration, demonstrate understanding of the specific problems that drove them to search, and then guide them toward something better.

This changes everything about how you write.

How to research a topic/keyword before writing it

Once you have your keyword and you understand the frustration behind it, you need to actually research the content you're going to write. This is where AI becomes genuinely useful—not for writing, but for gathering information efficiently.

The mistake people make is asking AI to research and write in one step. They paste in a keyword and ask for a complete article. This produces generic, surface-level content because the model is trying to do too much at once. It doesn't have enough information to write with depth, so it falls back on vague generalities and filler paragraphs. The output reads like it was written by someone who skimmed three blog posts and called it research.

Instead, separate research from writing completely. Research first, thoroughly. Build up a foundation of knowledge about every tool, every feature, every complaint users have. Then write, using what you've gathered. This separation is what allows you to write quickly without sacrificing depth—you're not asking the AI to figure out what to say while it's saying it.

The research phase has distinct stages, each with a specific purpose. I'll walk through the exact prompts I use for alternatives articles (like "7 Best Lokalise Alternatives"), then show how the sequence differs for review articles (like "Lokalise Review: Is It Worth It?").

Alternatives articles

Step 1: Initial landscape research

The first prompt casts a wide net. You're trying to understand what options exist and get a general sense of each one.

I want to write an article on "7 Best Lokalise Alternatives (2025)." Can you search the web to find these tools? Then we can do an outline and write. But please just do the research for now. Find their advantages, weaknesses, and features.

Notice what this prompt does. It tells the AI the exact article you're writing, which helps it understand the context. It explicitly says to search the web, which triggers the model to pull current information rather than relying on training data that might be outdated. And it specifically asks for advantages, weaknesses, and features—not just a list of tools.

The key instruction is "please just do the research for now." This prevents the AI from jumping ahead to writing. You want it to focus entirely on gathering information.

What you'll get back is a list of 7-10 tools with surface-level descriptions of each. This isn't enough to write with authority, but it tells you what tools to investigate further. Don't rush past this step. Ask follow-up questions if anything is unclear. If the AI mentions a tool you haven't heard of, ask it to dig deeper. If a weakness sounds vague, ask for specific examples from user reviews.

Step 2: Establish the writing philosophy

Before you start structuring content, you need the AI to understand how you think about content. Without this grounding, you'll get generic "What is localization?" sections that waste everyone's time.

Do you know pain point SEO? The idea is that we write for readers who already know they need a solution and are actively comparing options. They don't need education on the category—they need specific guidance on which option is right for their situation.

This prompt does something subtle but important. It's not asking the AI to do anything yet—it's teaching the AI a framework. When you later ask for an outline or draft, the model will remember this philosophy and apply it.

The AI will usually respond by confirming its understanding of pain point SEO and maybe adding some details. That's fine. The point is that you've calibrated its thinking before it starts producing output.

If you're familiar with the Grow and Convert writing style—which emphasizes writing for readers who are already in-market rather than educating top-of-funnel audiences—you can add that reference:

Do you know the Grow and Convert writing style? Also, how about pain point SEO?

This gives the AI two overlapping frameworks that will shape everything that follows.

Step 3: Generate the outline

Now you're ready to create structure. The outline determines the flow of your article, so you want it built on the philosophy you just established.

Based on pain point SEO principles, please put together a solid outline. Headings should be sentence case.

The instruction about sentence case is a small detail, but it matters. AI defaults to title case ("Best Tools For Developer Teams") which looks dated. Sentence case ("Best tools for developer teams") looks modern and matches how most successful content sites format their headings.

What you'll get back is a structured outline with H2 and H3 headings, organized around the tools you identified in step 1. Review this carefully. If the structure doesn't make sense—if tools are in a weird order, or if there's an unnecessary introductory section about "what is localization"—push back now. It's easier to fix structure problems before you've written thousands of words.

Step 4: Deep research on each tool

Here's where most people stop too early. They have an outline and a surface-level understanding of each tool, so they start writing. But surface-level understanding produces surface-level content.

For each tool you're going to cover, you need to go deeper. This means a separate research prompt for each one.

Search the web about Crowdin. I need to understand their specific features for developer teams, their pricing model, their integration options, and what users actually complain about in reviews.

This prompt is specific about what you need. Not just "tell me about Crowdin," but specific categories of information: features for a particular audience, pricing structure, integrations, and user complaints. The user complaints part is especially important—this is where you find the real weaknesses that make your content valuable, not the sanitized limitations that appear in the company's own documentation.

Do this for every tool in your article. Yes, it takes time. The research phase might take 30 to 40 minutes total. But this is what makes the writing phase fast and the content actually valuable. You can't write with authority about tools you don't understand, and you can't understand tools from a single surface-level search.

When the AI gives you information, verify anything that seems important. Pricing changes. Features get deprecated. If the AI says Crowdin costs $25/month for small teams, go to Crowdin's pricing page and confirm. AI makes things up, and publishing wrong information destroys your credibility.

Review articles

Review articles (like "Lokalise Review: Is It Worth It?") follow a similar philosophy but with a different sequence. Instead of researching multiple tools broadly, you're researching one tool deeply.

Step 1: Initial product research

I'm writing a blog on "Lokalise Review." Can you please search the web about the features, pros and cons of Lokalise (https://lokalise.com/)?

Including the URL does two things. It confirms exactly which product you're writing about (there might be multiple tools with similar names), and it gives the AI a starting point for its search.

Step 2: Establish the framework

Same as alternatives articles—you need the AI to understand pain point SEO before it starts structuring content.

Do you know the Grow and Convert writing style? Also, how about pain point SEO?

Step 3: Research standout features

Now you go deeper on the positive side. You want to understand not just what features exist, but which features users actually care about.

Now search the web about the top 3 Lokalise key standout features. Only features—I need to understand what the product actually does well.

The instruction "only features" prevents the AI from padding the response with generic commentary. You want specific capabilities, not opinions about the company's mission statement.

Step 4: Research limitations

This is where review articles get valuable. Anyone can list features from a product's marketing page. The differentiated insight comes from understanding what doesn't work.

Now search the web about these limitations of Lokalise—what do users complain about in reviews and forums?

The phrase "in reviews and forums" is important. You're directing the AI to look for real user feedback, not just theoretical weaknesses. G2, Capterra, Reddit threads, Hacker News discussions—these are where you find the complaints that actual users have, not the polished objection-handling from the company's sales team.

Step 5: Research pricing

For review articles, pricing deserves its own research step because readers searching for "[Product] review" almost always want to know if it's worth the cost.

Now search the web about Lokalise pricing.

After the AI returns pricing information, verify it directly on the product's website. Pricing is one of the things AI gets wrong most often because it changes frequently and companies structure it in complex ways.

Why this separation matters

The entire research phase might take 30-45 minutes. That feels slow when you're eager to start writing. But here's what that investment buys you:

When you start writing, you won't be stopping every paragraph to figure out what to say next. You'll have a foundation of knowledge about each tool—not just features, but specific use cases, real limitations, pricing quirks, integration details. The writing phase becomes a matter of organizing and articulating what you already know, which is dramatically faster than trying to research and write simultaneously.

The content itself will be better. You'll include details that other articles miss because other writers didn't do this level of research. You'll have specific examples to support your claims. You'll know which complaints are common and which are edge cases, so your criticism will be calibrated rather than arbitrary.

And you'll write with confidence. Nothing makes writing slower than uncertainty. When you're not sure if something is true, you hedge. You write "may be useful for some teams" instead of "works well for remote teams managing multiple languages." Hedging makes content weak. Research makes hedging unnecessary.

What a good introduction looks like and how to write one using AI

The introduction is where most content fails, and it fails in predictable ways.

Writers treat introductions as warm-ups, a place to ease into the topic before getting to substance. But the reader isn't looking for a warm-up. They clicked on your article because the title promised to solve their problem. The introduction needs to confirm they're in the right place and then get out of the way. The whole thing should take three to six sentences.

Here are examples of introductions that don't work.

• The generic statement opener that starts with something no one could disagree with.

"Managing localization across multiple languages can be challenging for growing development teams."

Does anyone searching for localization tools not already know this? They're searching precisely because they're already dealing with the challenge. You've wasted their time telling them something they knew before they arrived.

• The statistics opener that tries to establish importance through numbers.

"The global language services market is projected to reach $96 billion by 2027."

The reader doesn't care. They're not researching market sizes. They're trying to pick a tool. The statistic adds nothing to their decision and signals that you're padding your word count.

• The question opener that attempts to hook through false engagement.

"Have you ever struggled to keep your translations in sync across multiple platforms?"

This is supposed to create connection, but it just feels manipulative. Of course they've struggled. That's why they're searching. Asking the question doesn't demonstrate insight. It demonstrates that you don't have anything more valuable to say.

• The landscape opener that goes so far back it insults the reader's intelligence.

"In today's interconnected global marketplace, the ability to reach customers in their native language has become essential."

Anyone searching for i18n tools already knows why localization matters. Starting here suggests you think your reader is stupid, or that you yourself don't know enough about the topic to start somewhere more specific.

All of these introductions share the same underlying problem: they're written for a hypothetical reader who knows nothing, rather than the actual reader who knows plenty and just wants specific guidance.

The introduction that works does something different. It calls out the specific frustration that brought the reader here, lists the particular pain points they might be experiencing, and then tells them exactly what the article will deliver.

Here's the structure I use:

The first sentence names the tool or situation the reader is evaluating. This confirms immediately that they've found the right article. The first sentence should always contain the name of the tool you're writing about. Then come two or three sentences describing specific pain points—not generic problems, but the actual issues that would make someone search for alternatives. Finally, a sentence explaining what the article will cover so the reader knows whether to keep going.

Here's the prompt I use to generate introductions:

I am writing an introduction for the article "7 Best Lokalise Alternatives (2025)". Using the below guide on how to write an intro, please write me a solid introduction.

Key rules:
- Avoid sentences with 2 prepositions
- One sentence should carry one idea
- Be clear so the pain point you're mentioning resonates
- Avoid being clever
- The first sentence should contain the name of the tool we are writing about

The template I use:
[Main pain point]
[Pain point 1]
[Pain point 2]
[Pain point 3]
[Introducing the article body]

The first line should always be an industry problem you're calling out. Then follows three sentences going deep on the problem you called out on the previous line. Then comes the last part, where you introduce the article.

And here's what good output looks like:

Lokalise has become a popular choice for managing translations in software projects—but it's not the right fit for everyone.

Maybe you've found that pricing escalates quickly once you move beyond the basics. Or you want tighter integration with your CI/CD pipeline without manual configuration. Or perhaps the interface feels too heavyweight when your team just needs a simple way to manage keys and ship translations.

If any of that sounds familiar, this guide is for you. In this article, we'll walk through 7 of the best Lokalise alternatives, what makes each one stand out, and how to choose the right fit based on your workflow, team size, and technical requirements.

Notice what this does. It names Lokalise in the first sentence, confirming the reader is in the right place. It lists three specific frustrations—pricing, CI/CD integration, and interface complexity—which are the actual reasons someone would search for alternatives. And it tells them exactly what the article will deliver. The reader who has those frustrations will keep reading. The reader who doesn't will leave, which is fine. They weren't going to convert anyway.

How to write the other sections of the blog post

You've done your research. You have an outline. You understand each tool's features, weaknesses, pricing, and what real users complain about. Now you're ready to write.

But you don't write the whole article at once. You write section by section, maintaining quality control at each step. This approach is slower than asking for a complete draft, but it produces dramatically better output. When you write section by section, you can catch problems early—bad flow, missing depth, inaccurate claims—before they compound across the entire article.

There's a critical mechanical detail that trips people up: when you ask the AI to write a section, you need to feed it the research you gathered earlier. The AI doesn't automatically remember what it found three prompts ago. If you just say "write the section about Crowdin," you'll get generic output because the model is working from its general training data, not from the specific research you gathered.

The workflow looks like this: you did research on Crowdin and got back detailed information about features, pricing, integrations, and user complaints. You saved that output (I keep mine in a simple text file or just scroll up in the conversation). Now when you ask the AI to write the Crowdin section, you paste that research into the prompt so the AI has all the context it needs.

Let me walk through exactly how this works for alternatives articles and review articles.

Alternatives articles

For an alternatives article, each tool gets its own section following a consistent format. The format matters because readers scanning for specific tools should be able to find them quickly, and the parallel structure makes comparisons easier.

Here's the complete prompt I use for the first tool section. This is long because it needs to establish all the formatting and quality standards:

Here's the research on Crowdin:

[Paste your research output here—features, pricing, integrations, user complaints, everything the AI found in your earlier research prompt]

Now leave search mode and write this section. Be thorough and in-depth. Be logical—ask yourself how each sentence is adding value and why it deserves to be in this section. Each sentence should read like the next logical step of the previous.

Avoid sentences shorter than 8 words. Avoid sentences with two prepositions. Use 5th grade level English.

Use this format:

H2 Crowdin: Best Lokalise alternative for [specific use case based on research]

H3 Key Crowdin standout features

- Key feature 1
- Key feature 2
- Key feature 3
- Key feature 4
- Key feature 5

[2 paragraphs on what Crowdin does well that differentiates it from Lokalise]

[Transition paragraph, then 2 paragraphs on weaknesses]

[Comparison table with Lokalise]

Stick to this format at 60% and add your own touch at 40%. Focus on features and use cases rather than fluff.

Let me break down why each instruction matters.

"Here's the research on Crowdin: [paste research]" — This is the most important part. Without the research context, the AI writes from general knowledge, which produces the same generic content that's already ranking on page one. With your research, it writes from specific, current information about features users actually care about, limitations users actually complain about, and pricing that's actually accurate.

"Now leave search mode" — Some AI tools (like ChatGPT with browsing or Claude with web search) distinguish between searching and writing. This instruction tells the model to stop gathering information and start producing prose. If you skip this, the model might do another search instead of writing, or it might write tentatively because it thinks more research might be coming.

"Be thorough and in-depth" — AI's default mode is to skim the surface. It will give you a sentence or two per feature unless you explicitly ask for depth. This instruction pushes it to explain not just what a feature is, but why it matters and how it works in practice.

"Be logical—ask yourself how each sentence is adding value and why it deserves to be in this section" — This instruction sounds abstract, but it has a concrete effect. It makes the AI more selective. Without it, the model pads content with filler sentences that don't advance the reader's understanding. With it, each sentence earns its place.

"Each sentence should read like the next logical step of the previous" — This is the single most important instruction for quality. AI naturally produces lists of facts that don't connect to each other. This instruction forces it to create flow—to build each idea on the previous one so the reader is carried through the section rather than bouncing between disconnected points. I'll show you what the difference looks like in the next section.

"Avoid sentences shorter than 8 words" — Short sentences aren't inherently bad, but AI overuses them. It produces choppy, staccato prose that reads like bullet points converted to paragraphs. This instruction forces more substantive sentence construction.

"Avoid sentences with two prepositions" — Sentences with multiple prepositions get convoluted. "The tool for teams in companies with global operations" is harder to parse than "The tool works well for global teams." This instruction pushes toward cleaner syntax.

"Use 5th grade level English" — This isn't about dumbing down content. It's about clarity. Complex ideas explained in simple language are more accessible and more persuasive than the same ideas buried in jargon. Readers don't want to work hard to understand your point. They want you to make understanding easy.

"Stick to this format at 60% and add your own touch at 40%" — This gives the AI permission to deviate from the template when it makes sense. Maybe a particular tool has an unusual pricing model that deserves its own paragraph. Maybe the comparison table needs an extra column. Rigid adherence to format produces robotic content. This instruction balances consistency with flexibility.

"Focus on features and use cases rather than fluff" — "Fluff" is the generic filler that AI loves: "In today's fast-paced business environment..." or "With the rise of global commerce..." This instruction cuts it out. Every sentence should be about specific features or specific use cases, not abstract commentary.

The output you get from this prompt will be a complete section—typically 400-600 words—covering the tool's standout features, what it does better than the main competitor, its weaknesses, and a comparison table. Review it carefully. Check that the features mentioned actually exist (AI hallucinates). Check that the pricing is current. Check that the flow feels natural when you read it aloud.

Writing subsequent tool sections

Once you've established the format with the first tool, you don't need to repeat all the instructions. The AI remembers the style and structure from the previous section.

For the second tool and beyond, the prompt is simpler:

Here's the research on Phrase:

[Paste your research output for Phrase]

Using the same format and writing style as the Crowdin section, write this section about Phrase.

This maintains consistency across the article without you having to paste the full formatting instructions every time. The AI will mirror the structure, tone, and depth of the first section.

One important note: "the same format and writing style" only works if the first section was good. If you weren't happy with the Crowdin section—if the flow was choppy or the depth was shallow—fix it before moving on. Otherwise you're asking the AI to replicate problems across the entire article.

Review articles

Review articles serve a different reader than alternatives articles. Someone searching "Lokalise review" isn't comparing seven tools. They've already narrowed down to one. They're probably close to a purchase decision and want to validate that choice—or find reasons to reconsider. They want depth on a single product: what it actually does, what it does well, what it does poorly, and whether the price is justified.

This means the structure is different. Instead of seven shallow sections covering seven tools, you have one deep article covering one tool from multiple angles. The sections build on each other to give the reader a complete picture.

Here's the structure I use for review articles:

1. Product description (2 paragraphs) — What the product actually does
2. Transition to limitations (1 paragraph) — Signal that this isn't a puff piece
3. Features section (3-4 H3s under one H2) — Deep dive on what users love
4. Limitations section (3-4 H3s under one H2) — Deep dive on what users complain about
5. Pricing section (one H2) — Is it worth the cost, and for whom?

Each section requires its own prompt, and each prompt builds on the research you gathered earlier. Let me walk through how to write each one.

Section 1: The product description

The article opens by explaining what the product does. This sounds obvious, but most review articles get it wrong. They start with industry context ("The localization market is growing rapidly...") or problem framing ("Managing translations across multiple platforms is challenging..."). The reader doesn't need this. They already know the industry exists and the problem is real—that's why they're searching for a review of a specific solution.

Start with the product itself. No preamble.

Here's the research on Lokalise:

[Paste your initial research—features, core functionality, what the product does]

Using pain point SEO principles, please write a two paragraph description of Lokalise. Please only write about what Lokalise does—do not add anything else. People searching for "Lokalise review" already know the i18n landscape is changing. They only want to read about Lokalise specifically.

The instruction "do not add anything else" prevents the AI from padding with context the reader doesn't need. The output should be two tight paragraphs: what Lokalise is and what it does. No throat-clearing. No industry trends. Just the product.

Section 2: The transition to limitations

After describing the product, you need to signal that this review will be balanced. Readers are skeptical of content that only says positive things—they assume it's marketing. Acknowledging limitations early earns trust and keeps people reading.

This is a short section—just one paragraph—that bridges from "here's what it does" to "here's where it falls short":

Now write a third paragraph that goes: "Despite [reference the strengths you just described], Lokalise has limitations like [brief preview of weaknesses]. In this article, we'll cover..."

The template structure ensures the AI acknowledges strengths (establishing credibility) while previewing limitations (establishing balance). It also sets up the article structure so readers know what's coming.

Section 3: Features deep dive

Now you go deep on the capabilities that actually matter. During research, you identified the three or four features users consistently praise—the ones that differentiate this product from competitors. This section explains each one in detail.

Here's the research on Lokalise's standout features:

[Paste your research on features—what they do, how they work, why users value them]

Now let's write an H2: Three key features users love about Lokalise

Write a small intro paragraph between this H2 and the first H3, then write each feature section:

H3 Over-the-air translation updates

H3 Visual context for translators

H3 Branching and version control for translations

Now leave search mode and write this section. Be thorough and in-depth under each feature. Get into the nitty gritty of what it actually does—not just that the feature exists, but how it works in practice and why it matters to the user.

Each sentence should read like the next logical step of the previous. Make it flow well so the dots are connected. Ideas should build on previous ones. Avoid sentences shorter than 8 words. Avoid sentences with two prepositions.

The key phrase is "get into the nitty gritty." AI defaults to surface-level descriptions: "Lokalise offers over-the-air updates that let you push translations without releasing a new app version." That's accurate but shallow. The nitty gritty version explains how the SDK integrates, how translations are cached locally, what happens when a user's device is offline, how you roll back a bad translation. This depth is what makes the review actually useful.

Section 4: Limitations deep dive

This is where the review earns its credibility. Anyone can list features from a marketing page. Honest criticism requires research, judgment, and willingness to say things the company wouldn't want published.

Here's the research on Lokalise's limitations:

[Paste your research on user complaints from forums, G2, Capterra, Reddit]

Now let's write an H2: Three key limitations users mention about Lokalise

Write a small intro paragraph, then write each limitation section:

H3 Pricing complexity at scale

H3 Learning curve for non-technical users

H3 Limited offline functionality

Now leave search mode and write this section. Be thorough and in-depth under each limitation. Get into the nitty gritty of why it matters—not just that the limitation exists, but how it affects real workflows and what users actually experience.

Each sentence should read like the next logical step of the previous. Make sure the dots are connected and that it's not truncated. Use 5th grade writing level.

The phrase "why it matters" is critical. "Pricing complexity at scale" is meaningless without explanation. What does complexity mean? At what point does pricing become a problem—10 users? 100? How much more expensive is it compared to alternatives at that scale? What do users actually say when they hit this issue? The nitty gritty transforms vague complaints into useful buyer guidance.

Section 5: Pricing analysis

Pricing deserves its own section because it's usually the deciding factor. Readers who've made it this far want to know: is this product worth what it costs?

Here's the research on Lokalise pricing:

[Paste your pricing research—tier structure, what's included at each level, what users say about value]

Now write this section with an H2: "Lokalise Pricing: Is It Worth It?"

Cover both the good and the bad. Explain the tier structure clearly. Include specific numbers. Address whether the pricing is competitive for what you get. Be direct about who should and shouldn't pay for this product at each tier.

The instruction "be direct about who should and shouldn't pay" forces a recommendation. Readers don't want "it depends on your needs." They want guidance: "If you're a small team with less than 10,000 strings, the free tier is probably sufficient. If you're managing multiple products with shared translation memory, the Team plan is worth the upgrade for collaboration features. If you're an enterprise that needs SSO and audit logs, budget for Enterprise but negotiate—their list pricing is flexible."

Quality checklist: what separates readable content from SEO filler

After each section, pause and evaluate before moving on.

Read the section aloud. Where you stumble is where the flow is broken. If you trip over a sentence, readers will too. Mark those sentences for revision.

Check specific claims against your research and against the actual product. If the AI says a feature works a certain way, verify it. If pricing is mentioned, confirm it's current. AI hallucinates details, and publishing wrong information destroys credibility.

Ask yourself: would this section be useful to someone actually trying to make a decision? If it's just describing features without explaining why they matter, it's not useful. If it's hedging every opinion with "may be valuable for some users," it's not useful. Push the AI to be more direct, more specific, more opinionated.

If a section isn't good enough, don't move on. Fix it first. Use the revision prompts I'll cover in the next section. Getting one section right sets the standard for the rest of the article. Getting one section wrong propagates problems throughout.

Fix flow before you fix anything else

The critical instruction in all these prompts is "each sentence should read like the next logical step of the previous." This is what separates readable content from a pile of facts arranged into paragraphs.

Here's what bad flow looks like:

"Crowdin supports over 40 file formats. The platform offers machine translation integration. Teams can use the API to automate workflows. There's also a real-time collaboration feature."

Each sentence introduces a new fact with no connection to the previous fact. The reader has to do the mental work of figuring out how these pieces relate to each other and why they should care. Most readers won't bother. They'll skim, get frustrated, and leave.

Here's the same information with proper flow:

"Crowdin supports over 40 file formats out of the box, which means you can usually connect your existing project without reformatting anything. Once your files are synced, the platform can apply machine translation as a starting point, saving your human translators from working on blank strings. From there, teams collaborate in real-time—translators see each other's changes immediately, which prevents the version conflicts that plague email-based workflows. And because the entire process is API-driven, you can wire it into your deployment pipeline so translations ship automatically whenever your code does."

Same features. But now each sentence follows logically from the one before. The reader understands not just what Crowdin does, but why each feature matters and how they connect to each other and to the reader's workflow.

When you get a draft back from AI, this flow is the first thing you check. AI's default mode is to list facts. It will give you the disconnected version almost every time on the first try. When that happens, push back:

Let's come back to this section. Each sentence reads like it's introducing a new feature, which is fine. But the sentences feel isolated from each other. It's hard to connect the dots. They don't build on each other, which makes the section hard to follow through. Please rewrite so each sentence flows into the next.

You'll often need to use this prompt multiple times before a section flows properly. The iteration is worth it, because content that flows keeps readers on the page, and readers who stay on the page are readers who might convert.

Sometimes the AI will improve the flow but lose depth. When that happens:

Better flow, but it lacks depth now. Each point feels surface-level. Go deeper on why each feature matters and how it actually works in practice.

The back-and-forth is part of the process. You're not trying to get perfect output on the first try. You're using the AI as a drafting partner that you push and refine until the content meets your standards.

Have an opinion, or you are wasting the readers' time

The internet is full of hedging. "It depends on your needs." "Both options have pros and cons." "The best choice varies by situation." This kind of writing is useless to someone trying to make a decision. What they want is for you to just tell them what to do.

If you've actually used the tools you're writing about—or if you've done thorough research about what real users say—you have opinions about them. Share those opinions directly, with reasoning to back them up.

This doesn't mean being unfair to competitors. In fact, being fair to competitors is part of what makes content trustworthy. If you trash-talk every alternative to your product, readers will correctly identify you as biased and discount everything you say. But if you honestly explain when a competitor is the right choice and when they're not, readers will trust your judgment when you recommend your own product.

The structure I use for each tool section forces this kind of clarity. After describing the standout features, I write two paragraphs on what the tool does well that differentiates it from the main competitor. Then I write a transition and two paragraphs on weaknesses. Then I include a comparison table that makes the tradeoffs scannable. This structure makes hedging almost impossible. You can't describe what a tool does well and then immediately describe its weaknesses without having opinions. And those opinions, expressed clearly with reasoning, are exactly what readers are looking for.

Add information the SERP does not already have

Google has started talking about something they call "information gain"—the degree to which a piece of content adds new information beyond what already exists on the topic. If your article says the same things as every other article on the same keyword, there's no particular reason for Google to rank you above them. But if you include things other articles don't, you're giving Google a reason to put you at the top.

Information gain can come from several sources.

Original data is the most obvious. If you can include statistics or insights that come from your own product or research, that's automatically unique.

Screenshots from inside competitor products are valuable because most articles just describe features in text. Actually showing the interface is more useful and harder to replicate.

Pricing information that's difficult to find is valuable because many companies hide their pricing, and readers are desperate to know what things actually cost.

Real user complaints from review sites and forums add credibility because you're not just describing features, you're describing how those features work in practice.

The bar for information gain is lower than most people think. Most content is so generic that even basic original insight stands out. But you do have to be intentional about it. Before publishing any article, ask yourself: what does this piece contain that no other article on this keyword contains? If you can't answer that question, the article probably won't rank.

Make it scannable with structural elements

Beyond the content itself, there are structural elements that every article needs.

Tables make comparison information scannable, and Google loves featuring them in snippets. Any time you're comparing options—features, pricing, pros and cons—put that information in a table, not in prose. Readers can process tables faster, and Google can extract structured data from them.

At the end of an alternatives article, I always add a summary table:

Here's the full article. Please create a TL;DR comparison table showing all 7 alternatives with columns for: Best For, Starting Price, Key Strength, Main Limitation.

This gives readers who want to skim a quick way to orient themselves, and it gives Google a structured summary to potentially feature.

Internal links distribute authority across your site and help Google understand your content structure. Every article should link to at least ten other articles on your site. I keep a spreadsheet tracking which posts link to which other posts, and I check it every time I publish. This sounds tedious, and it is, but internal linking is one of the highest-leverage SEO activities you can do.

Images should add value, not decoration. Screenshots, diagrams, comparison charts—these help readers understand your content and make your articles more linkable. Stock photos of people smiling at laptops waste bandwidth and make you look like every other generic blog on the internet.

Do the editing pass that removes the AI smell

AI doesn't write final drafts. It writes first drafts that require substantial editing. This is critical to understand. If you publish AI output directly, readers can tell, and Google is getting better at detecting it.

The editing pass is where you read each section out loud to catch places where you stumble—that's where the flow is broken. It's where you add original insight, the things you know about this topic that the AI doesn't, the perspective that comes from actually working in this space. It's where you remove hedging, cutting every "may be useful" and "could potentially" and replacing it with clear recommendations. It's where you verify facts, because AI makes things up and every specific claim about pricing, features, or integrations needs to be checked against the actual product.

The editing pass typically takes 20 to 30 minutes per article. That time is non-negotiable. Skipping it turns your content machine into a garbage machine, and garbage doesn't rank or convert no matter how much of it you publish.

Put it all together: the complete workflow

Let me pull this together into the workflow I actually use.

Batch research day (2-3 hours for a week's worth of content):

Pick 5 keywords from your prioritized list. Run research prompts for all 5. Generate outlines for all 5. Do deep research on each tool or topic you'll cover. Organize your notes so they're ready when you write.

Daily writing (60-90 minutes per article):

Open your research notes for today's keyword. Generate the introduction using the pain point template. Write each section using the appropriate prompt. Push back on flow issues until each section reads smoothly. Ask for the summary comparison table. Do the editing pass—read aloud, add insight, remove hedging, verify facts. Add internal links and images. Publish.

Weekly review (30 minutes):

Check Search Console for which articles are getting impressions. Identify articles ranking positions 5-15—these are in the opportunity zone where small improvements can push them onto page one. Update those articles with additional content, better optimization, or more internal links pointing to them.

At the 20-articles-per-month pace I mentioned earlier, you're spending roughly 25-30 hours on writing and 2-3 hours on batch research. Add another 2-3 hours for weekly review and optimization. Call it 30-35 hours per month total.

That's a significant investment. But you're building an asset that will generate returns for years. And unlike the TikTok content that dies in 48 hours, every article you publish this way continues working for you indefinitely.

The content you produce using this system won't win literary awards. But it will rank, it will convert, and it will compound. That's what matters.