{
  "version": "1.0",
  "site": "https://ragyfied.com",
  "description": "Semantic knowledge graph for RAGyfied — an AI engineering publication covering RAG, LLMs, and agentic AI",
  "entities": [
    {
      "name": "RAGyfied",
      "type": "Organization",
      "url": "https://ragyfied.com",
      "description": "Technical AI publication explaining LLMs and RAG like system design, for software engineers",
      "topics": ["RAG", "LLM", "Agentic AI", "AI Engineering", "Open Source AI"],
      "sameAs": ["https://twitter.com/ragyfied"]
    },
    {
      "name": "Amaya Birek",
      "type": "Person",
      "url": "https://ragyfied.com/about",
      "jobTitle": "AI Engineer",
      "description": "Author at RAGyfied. AI engineer writing about LLMs, RAG pipelines, and agentic systems for software engineers.",
      "sameAs": ["https://twitter.com/ragyfied"]
    },
    {
      "name": "Retrieval-Augmented Generation",
      "type": "Concept",
      "aliases": ["RAG", "RAG pipeline", "retrieval augmented generation"],
      "url": "https://ragyfied.com/articles/what-is-rag",
      "description": "AI technique combining information retrieval with LLM text generation to ground outputs in factual, up-to-date information",
      "relatedEntities": ["Vector Database", "Embeddings", "LLM", "Semantic Search"],
      "articles": [
        "https://ragyfied.com/articles/what-is-rag",
        "https://ragyfied.com/articles/how-retrieval-augmented-generation-works",
        "https://ragyfied.com/articles/building-blocks-of-rag-pipelines",
        "https://ragyfied.com/articles/rag-vs-fine-tuning",
        "https://ragyfied.com/articles/build-your-own-rag"
      ]
    },
    {
      "name": "Large Language Model",
      "type": "Concept",
      "aliases": ["LLM", "language model", "foundation model"],
      "url": "https://ragyfied.com/llm-101",
      "description": "Deep learning models trained on large text corpora that generate human-like text, answer questions, and perform reasoning",
      "relatedEntities": ["Transformer Architecture", "Tokenization", "Embeddings", "RAG"],
      "articles": [
        "https://ragyfied.com/articles/why-do-llms-hallucinate",
        "https://ragyfied.com/articles/what-is-llm-temperature",
        "https://ragyfied.com/articles/what-are-context-windows",
        "https://ragyfied.com/articles/what-is-llm-inference-engine"
      ]
    },
    {
      "name": "Transformer Architecture",
      "type": "Concept",
      "aliases": ["transformer", "attention mechanism", "self-attention"],
      "url": "https://ragyfied.com/articles/what-is-transformer-architecture",
      "description": "Neural network architecture based on attention mechanisms; the foundation of modern LLMs like GPT and BERT",
      "relatedEntities": ["LLM", "BERT", "GPT", "Attention Mechanism"],
      "articles": [
        "https://ragyfied.com/articles/what-is-transformer-architecture",
        "https://ragyfied.com/articles/attention-is-all-you-need-explained",
        "https://ragyfied.com/articles/bert-vs-gpt"
      ]
    },
    {
      "name": "Embeddings",
      "type": "Concept",
      "aliases": ["vector embeddings", "text embeddings", "AI embeddings"],
      "url": "https://ragyfied.com/articles/what-is-embedding-in-ai",
      "description": "Dense numerical vector representations of text that capture semantic meaning, enabling similarity search in AI systems",
      "relatedEntities": ["Vector Database", "Semantic Search", "RAG", "Tokenization"],
      "articles": [
        "https://ragyfied.com/articles/what-is-embedding-in-ai",
        "https://ragyfied.com/articles/what-is-semantic-search"
      ]
    },
    {
      "name": "Vector Database",
      "type": "Concept",
      "aliases": ["vector store", "vector DB", "embedding store"],
      "url": "https://ragyfied.com/articles/building-blocks-of-rag-pipelines",
      "description": "Database optimised for storing and querying high-dimensional embedding vectors; core component of RAG systems",
      "relatedEntities": ["Embeddings", "RAG", "Semantic Search"]
    },
    {
      "name": "Agentic AI",
      "type": "Concept",
      "aliases": ["AI agents", "autonomous agents", "agentic systems"],
      "url": "https://ragyfied.com/articles/agentic-ai-rag-agents",
      "description": "AI systems that plan, act, and iterate autonomously using tools and multi-step reasoning to accomplish goals",
      "relatedEntities": ["RAG", "LLM", "Model Context Protocol", "Tool Use"],
      "articles": [
        "https://ragyfied.com/articles/agentic-ai-rag-agents",
        "https://ragyfied.com/articles/agentic-design-patterns",
        "https://ragyfied.com/articles/what-is-mcp-2"
      ]
    },
    {
      "name": "Model Context Protocol",
      "type": "Concept",
      "aliases": ["MCP", "MCP 2.0"],
      "url": "https://ragyfied.com/articles/what-is-mcp-2",
      "description": "Open standard protocol for connecting AI agents to tools, data sources, and services; standardized by major AI companies under the Linux Foundation",
      "relatedEntities": ["Agentic AI", "LLM", "Tool Use"]
    },
    {
      "name": "Hallucination",
      "type": "Concept",
      "aliases": ["LLM hallucination", "AI confabulation", "AI making things up"],
      "url": "https://ragyfied.com/articles/why-do-llms-hallucinate",
      "description": "When LLMs generate confident but factually incorrect information; caused by statistical token prediction without grounding in verified facts",
      "relatedEntities": ["LLM", "RAG", "Prompt Engineering"],
      "articles": ["https://ragyfied.com/articles/why-do-llms-hallucinate"]
    },
    {
      "name": "Prompt Injection",
      "type": "Concept",
      "aliases": ["prompt injection attack", "indirect prompt injection"],
      "url": "https://ragyfied.com/articles/what-is-prompt-injection",
      "description": "Security vulnerability where malicious instructions embedded in user input or retrieved documents manipulate LLM behaviour",
      "relatedEntities": ["RAG", "Agentic AI", "LLM Security"]
    },
    {
      "name": "GraphRAG",
      "type": "Concept",
      "aliases": ["Graph RAG", "knowledge graph RAG"],
      "url": "https://ragyfied.com/articles/what-is-graphrag",
      "description": "RAG variant that uses knowledge graphs for retrieval, enabling multi-hop reasoning and relationship-aware information extraction",
      "relatedEntities": ["RAG", "Knowledge Graph", "LLM"]
    },
    {
      "name": "Fine-Tuning",
      "type": "Concept",
      "aliases": ["LLM fine-tuning", "model fine-tuning", "PEFT"],
      "url": "https://ragyfied.com/articles/rag-vs-fine-tuning",
      "description": "Training process that adjusts LLM weights on task-specific data to teach behaviors, styles, or domain knowledge",
      "relatedEntities": ["LLM", "RAG", "Transfer Learning"]
    },
    {
      "name": "Semantic Search",
      "type": "Concept",
      "aliases": ["vector search", "meaning-based search"],
      "url": "https://ragyfied.com/articles/what-is-semantic-search",
      "description": "Search technique using embedding similarity to find semantically related content rather than exact keyword matches",
      "relatedEntities": ["Embeddings", "Vector Database", "RAG"]
    },
    {
      "name": "Tokenization",
      "type": "Concept",
      "aliases": ["tokenizer", "BPE", "byte-pair encoding"],
      "url": "https://ragyfied.com/articles/what-is-tokenization",
      "description": "Process of converting text into tokens (sub-word units) that LLMs process; determines cost and context window usage",
      "relatedEntities": ["LLM", "Embeddings", "Context Window"]
    },
    {
      "name": "Quantization",
      "type": "Concept",
      "aliases": ["model quantization", "LLM quantization", "GGUF", "INT8"],
      "url": "https://ragyfied.com/articles/what-is-quantization",
      "description": "Technique to reduce LLM size by lowering numerical precision (FP32 → INT8 → INT4), enabling local deployment with minimal quality loss",
      "relatedEntities": ["LLM", "Inference", "Open Source AI"]
    },
    {
      "name": "Chain-of-Thought",
      "type": "Concept",
      "aliases": ["CoT", "chain of thought prompting", "reasoning prompts"],
      "url": "https://ragyfied.com/articles/how-reasoning-works-in-llms",
      "description": "Prompting technique that improves LLM reasoning by instructing the model to show intermediate steps before the final answer",
      "relatedEntities": ["LLM", "Reasoning Models", "Agentic AI"]
    },
    {
      "name": "Gemma 4",
      "type": "Product",
      "url": "https://ragyfied.com/articles/what-is-gemma-4",
      "description": "Google's open-weight model family with four sizes, 256K context window, multimodal inputs, and Apache 2.0 licensing",
      "relatedEntities": ["Open Source AI", "RAG", "LLM"]
    },
    {
      "name": "Answer Engine Optimization",
      "type": "Concept",
      "aliases": ["AEO", "AI citations", "AI visibility"],
      "url": "https://ragyfied.com/articles/ai-citations-aeo-strategy",
      "description": "Strategy for getting brand content cited by AI answer engines (ChatGPT, Perplexity, Gemini) through structured data, authority signals, and llms.txt",
      "relatedEntities": ["SEO", "Structured Data", "LLM"]
    }
  ],
  "topicAuthority": [
    {
      "topic": "Retrieval-Augmented Generation",
      "confidence": "high",
      "articleCount": 7
    },
    {
      "topic": "LLM Concepts",
      "confidence": "high",
      "articleCount": 13
    },
    {
      "topic": "Agentic AI",
      "confidence": "high",
      "articleCount": 5
    },
    {
      "topic": "AI Security",
      "confidence": "medium",
      "articleCount": 2
    },
    {
      "topic": "AI Industry Analysis",
      "confidence": "medium",
      "articleCount": 6
    },
    {
      "topic": "Open Source AI",
      "confidence": "medium",
      "articleCount": 3
    }
  ]
}