A simple but effective method to feed context into an AI project is to use the "Print to PDF" function on websites. This works well for company marketing pages, support articles, or competitor pricing, instantly turning structured web data into a usable file for the AI's knowledge base.
Websites now have a dual purpose. A significant portion of your content must be created specifically for AI agents—niche, granular, and structured for LLM consumption to improve AEO. The human-facing part must then evolve to offer deeper, more interactive experiences, as visitors will arrive with their basic research already completed by AI.
For professionals new to AI, the fastest way to get a tangible productivity boost is to use a paid plan like OpenAI's ($20) and create Custom GPTs. This low-barrier tool is exceptionally effective for automating repetitive tasks involving reading, summarizing, or transforming text.
People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.
Use an AI assistant like Claude Code to create a persistent corporate memory. Instruct it to save valuable artifacts like customer quotes, analyses, and complex SQL queries into a dedicated Git repository. This makes critical, unstructured information easily searchable and reusable for future AI-driven tasks.
Instead of guessing how to make your site more compatible with new AI browsers, directly ask the AI itself. Prompt ChatGPT with your URL and ask what changes are needed on your site to ensure the right answers appear when users search with the Atlas browser.
Instead of asking an AI to repurpose content ad-hoc, instruct it to build a persistent "content repurposing hub." This interactive artifact can take a single input (like a blog post URL) and automatically generate and organize assets for multiple channels (LinkedIn, Twitter, email) in one shareable location, creating a scalable content remixing system.
Instead of using siloed note-taking apps, structure all your knowledge—code, writing, proposals, notes—into a single GitHub monorepo. This creates a unified, context-rich environment that any AI coding assistant can access. This approach avoids vendor lock-in and provides the AI with a comprehensive "second brain" to work from.
To create a reliable AI persona, use a two-step process. First, use a constrained tool like Google's NotebookLM, which only uses provided source documents, to distill research into a core prompt. Then, use that fact-based prompt in a general-purpose LLM like ChatGPT to build the final interactive persona.
New AI-powered browsers struggle to index content locked in PDFs. To ensure your information is discoverable and summarized correctly by these tools, you must replicate gated content in standard, scannable HTML on your website.
AI-powered browsers can instantly open tabs for all your competitors and then analyze their sites based on your prompts. Ask them to compare pricing pages, identify email collection methods, or summarize go-to-market strategies to quickly gather competitive intelligence.