AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Retrieval-augmented generation (RAG) is a widely used approach for grounding large language models (LLMs) in external knowledge. However, configuring a pipeline is an expensive hyperparameter optimization problem over many interacting choices, from chunking and embedding model to reranking and generation. Existing opti...
ConRAG is introduced, which builds a lightweight entity-document graph from entity co-occurrence and LLM-based entity filtering that consistently improves bridge entity and reasoning chain recovery over strong RAG baselines, while reducing graph-indexing token cost by up to roughly 1.5 orders of magnitude.
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