Build robust retrieval-augmented generation pipelines using web search tools and large language models.
AI RAG Pipeline
Construct retrieval-augmented generation pipelines leveraging web search, content extraction, and LLMs for grounded, verified AI responses.
Skill impact
A focused workflow with measurable value.
Stars
709
Forks
96
Updated
Aug 24, 2026
About AI RAG Pipeline
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
What AI RAG Pipeline Can Help You Do
Helps you connect web search tools, extraction utilities, and large language models to execute multi-source research, fact-checking, and structured content analysis.
Common use cases
Building AI research assistants
Fact-checking claims against web evidence
Synthesizing industry and market reports
Extracting and summarizing web content
Creating grounded knowledge base queries
Quick start
Install command
npx skills add inference-sh/skills@ai-rag-pipelineTags
Topics and capabilities