Challenges: Too Smart for Google, Invisible to AI
Codewars was stuck in traditional content formats. Here’s what we uncovered:
1. LLMs couldn’t find their content
- Kata content was buried in dynamic JS
- No schema or semantic HTML. So, it was hard for LLMs to extract meaning
- Code examples had no structured steps or explanations
2. Missing visibility in zero-click AI answers
- ChatGPT, Gemini, and Copilot consistently showed results from Reddit, GeeksforGeeks, and Medium.
- Even branded queries like “solve Codewars recursion challenge in Python” didn’t surface official pages.
3. No content targeting prompt-style queries
- Content was dev-centric, not prompt-centric
- No pages formatted like typical AI queries (“How to solve [concept] in [language] with edge cases?”)
- No answer-first, structured walkthroughs that LLMs favor
4. No AI traffic measurement or strategy
- No referrer tracking from ChatGPT or Copilot
- The sitemap was flat. High-performing kata pages weren’t prioritized
- Internal linking was unstructured, so topic authority wasn't established
Strategy: The Growth Plan by ResultFirst
Building Content That AI Could Actually Find
We built an innovative, flexible system to help Codewars show up more often when people search on AI tools like ChatGPT, Copilot, and Gemini.
Here’s how we did it:
Step 1: Matched Kata Topics to Real Searches
We went through Codewars’ library of coding challenges and sorted them by things like programming language, skill level, and topic. Then we looked at how developers were actually asking for help on AI tools. For example:
- Best website for practicing programming
- Websites to practice coding
- Best Coding practice website
- Best Coding website for Practice
- Best websites to practice coding
We used those real questions to guide our content and title choices. Ultimately, we created over 400+ focused topics that answered specific, common developer questions, just as they’d ask them in AI tools.
The result?
Pages that actually answered what devs (and AI tools) were looking for.
Step 2: Made Pages LLM-Ready (Not Just SEO-Friendly)
We didn’t stop at writing better content. We structured it specifically for how large language models read the web. Here's how:
- Cleaned HTML Structure: We used clear, semantic tags so AI tools could follow the logic:
- ‹h2› for the main problem
- ‹h3› for each step in the solution
This made the flow easy to parse for tools like ChatGPT and Gemini.
- Schema That Speaks AI: We added Schema.org markup, such as FAQPage and CodeSample, so search engines and AI platforms could understand the content type and context, not just crawl the words.
- Smart Keyword Placement: We dynamically pulled in terms like language, concept, and difficulty level into the meta tags and H1s.
This helped us match not just SEO queries but also AI prompt formats. - Deployed LLM.txt: Updated the llm.txt file to allow large language models like ChatGPT, Copilot, Gemini, Perplexity, and others to crawl and prioritize key pages.
The Measurable Wins for Codewars: 3-Month Growth
AI Overview
Copilot
ChatGPT
Gemini
Why Our Strategies Worked
Ready to Be the Next AI-Smart Brand?
- ResultFirst helps fast-moving SaaS tools, dev platforms, and marketplaces get found by humans and LLMs.
- Same playbook. Same results. No guesswork.
- Your content may be good. We’ll make sure AI sees it.
- Start the conversation today.
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