Database Tools playbook

Query ChatGPT, Claude, and Gemini with 50+ intent-based prompts (e.g., 'What is the best DB for a real-time chat app?') and record brand mentions.

Priority: critical · Effort: low

Identify common misconceptions or outdated technical info the AI provides about your product.

Priority: critical · Effort: medium

Verify that documentation is accessible to Common Crawl and specific LLM user agents.

Priority: high · Effort: low

Analyze why competitors are recommended for specific use cases (e.g., 'Postgres for reliability').

Priority: medium · Effort: medium

Ensure your brand is correctly categorized as a 'Database' entity in Knowledge Graphs.

Priority: high · Effort: low

Educate DevRel and Product teams on the shift from SEO to AI Visibility.

Priority: medium · Effort: low

Ensure all docs are available in clean Markdown with clear H1-H4 hierarchies.

Priority: critical · Effort: medium

Implement SoftwareApplication and TechArticle schema.org JSON-LD on all pages.

Priority: critical · Effort: medium

Create 'Question/Answer' sections specifically designed to be extracted as direct answers by RAG systems.

Priority: high · Effort: low

Ensure all code examples use best practices and include comments that explain the 'why' for model training.

Priority: high · Effort: medium

Create 'Brand vs. Competitor' pages using objective, data-driven tables that AI can easily parse.

Priority: medium · Effort: high

Define proprietary features in the context of industry standards to improve entity linking.

Priority: medium · Effort: low

Explicitly allow GPTBot, Claude-Bot, and others to crawl documentation while blocking low-value pages.

Priority: critical · Effort: low

Increase 'Stars' and 'Forks' on main repos and starter kits to signal authority to models.

Priority: critical · Effort: high

Systematically answer top-tier questions related to your database category using your tool as the solution.

Priority: high · Effort: medium

Engage in r/database and r/programming to foster organic mentions in 'What should I use?' threads.

Priority: high · Effort: medium

Submit data to DB-Engines and Benchmarks.dev to establish objective performance authority.

Priority: medium · Effort: high

Publish technical deep-dives on The New Stack, InfoQ, and Hacker Noon.

Priority: medium · Effort: high

Get mentioned in curated Dev newsletters (e.g., TLDR, Pointer) to trigger secondary crawling.

Priority: low · Effort: medium

Ensure video tutorials have high-quality transcripts that mention the brand and use cases clearly.

Priority: medium · Effort: medium

Test how slight variations in user prompts affect your brand's ranking in AI recommendations.

Priority: high · Effort: medium

When models output wrong info, update the corresponding documentation with clear 'Warning' or 'Note' blocks.

Priority: critical · Effort: low

Test if different documentation structures lead to more accurate AI summaries.

Priority: medium · Effort: high

Prepare a 'clean' dataset of your docs for future partnerships with model providers (OpenAI/Anthropic).

Priority: medium · Effort: medium

Address specific negative 'cons' mentioned by AI by creating content that proves those issues are resolved.

Priority: high · Effort: medium

Test how tools like GitHub Copilot or Cursor suggest your DB driver in code completion.

Priority: high · Effort: medium

Set up weekly automated scripts to query LLMs for SoMV tracking.

Priority: high · Effort: medium

Ensure every new feature launch includes an 'AI Fact Sheet' for immediate ingestion.

Priority: medium · Effort: low

Partner with influencers to create content that 'trains' the social context of the models.

Priority: medium · Effort: high

Optimize your DB's management API for use by autonomous AI agents (e.g., clear OpenAPI specs).

Priority: high · Effort: high

Translate core docs into languages where models may lack local technical context (Spanish, Mandarin).

Priority: low · Effort: high