llm-context

To get pre-extracted web content for LLM grounding, you can use this Skill to retrieve structured text, tables, and code blocks directly. It bypasses raw HTML parsing, providing optimized data for AI agents to reason over real-time search results.

Yes, you can build a RAG pipeline with real-time search results using this Skill. It supplies pre-processed, relevance-ranked web content ideal for Retrieval Augmented Generation, enabling models to ingest up-to-date information efficiently.

The best way to extract text and tables for AI agents is retrieving pre-extracted content via GET or POST requests. This approach delivers structured data directly from web pages, optimized for immediate model ingestion and reasoning.

Yes, it supports custom ranking to control data sources for RAG applications. You can use Goggles to apply fine-grained control over the relevance and selection of web content used for grounding your AI models.

A limitation when using pre-extracted web content for LLM context is that adjustable context size parameters must be managed to fit model token limits. You are restricted to the extracted text, tables, and code provided, without raw HTML access.