What formats can you feed web data to AI?

AI models perform best with clean, structured formats like Markdown, JSON, and plain text. Markdown is optimal for most web content because it preserves semantic structure while minimizing token usage. JSON works well for structured datasets and fine-tuning tasks. Avoid feeding raw HTML or complex XML directly, as the extra markup creates noise that reduces accuracy and wastes processing tokens.

Feeding web data to AI models requires converting raw content into formats that language models can efficiently parse and understand. The three primary formats are Markdown (for text-heavy content with structure), JSON or JSONL (for structured data and training datasets), and plain text (for simple, unformatted content). Each format serves different use cases, but all share a common goal of presenting clean, organized information without unnecessary complexity.

Raw web data contains significant noise that degrades model performance. HTML includes navigation menus, advertisements, JavaScript code, and CSS styling that confuse language models and waste valuable context window space. A webpage’s HTML might consume 50,000 tokens, while the same content in Markdown uses only 5,000 tokens.

Format choice directly impacts three critical factors. Accuracy improves when models receive clean, structured input without extraneous markup. Processing efficiency increases as simpler formats require fewer tokens, allowing more actual content within context limits. Cost optimization follows naturally, since most AI APIs charge per token processed.

Markdown has become the standard format for feeding web content to AI models. The format uses simple syntax like headers, lists, and emphasis that language models easily interpret. Unlike HTML’s nested tags and attributes, Markdown presents information in a way that closely mirrors natural language.

Internal testing across multiple AI providers shows Markdown consistently outperforms HTML and XML for content understanding tasks. Models trained on Markdown can better identify document structure, extract relevant information, and maintain context across longer passages. The format strikes an ideal balance between human readability and machine parseability.

Web scraping tools like Firecrawl can automatically convert websites into clean Markdown, handling the complex task of removing navigation elements, ads, and boilerplate content while preserving the meaningful structure that helps AI models understand the content hierarchy.

JSON and its line-delimited variant JSONL work best for structured data scenarios. Fine-tuning datasets typically use JSONL format, with each line containing a complete training example including prompt and completion pairs. This structure allows models to learn specific response patterns and behaviors.

Tabular data from spreadsheets or databases converts well to JSON. Each row becomes a JSON object with clearly labeled fields, making it simple for AI models to understand relationships between data points. For retrieval augmented generation systems, JSON can store document chunks alongside metadata like source URLs and relevance scores.

The format excels when your AI application needs to process structured information like product catalogs, customer records, or API responses where maintaining field relationships is essential for accurate interpretation.

Start by removing all navigation elements, advertisements, footers, and sidebars before conversion. Clean HTML through an HTML parser that identifies the main content area, stripping away boilerplate that appears across multiple pages. This ensures your AI model focuses on unique, valuable information.

Convert the cleaned content to Markdown while preserving semantic elements like headings, which help models understand document hierarchy and importance. Chunk longer documents into logical sections that fit within your model’s context window, ensuring each chunk maintains enough context to be independently useful.

For Retrieval Augmented Generation applications, consider storing both the original source URL and the cleaned format. This allows you to provide citations and enables users to verify information against the source material.

Markdown provides the optimal balance of structure and simplicity for feeding web content to AI models. JSON works best for structured data and fine-tuning scenarios where field relationships matter. Always clean raw HTML before conversion, removing navigation and advertisements that add noise without value. Choose formats based on your specific use case: Markdown for content understanding, JSON for structured data, and plain text only for simple, unformatted information. Quality of input directly determines quality of AI output, making proper format selection essential for reliable results.