Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
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Learn how agentic RAG moves beyond static retrieval by letting AI agents plan queries, route across sources, and decide when retrieval is actually needed. Includes Python implementation with LangChain.
Explore how Graph RAG combines knowledge graphs with vector retrieval to answer multi-hop questions that standard RAG cannot. Covers graph construction, entity linking, and Microsoft GraphRAG.
Learn how to build a multi-index RAG system that routes queries to appropriate collections, merges results, and normalizes relevance scores across heterogeneous document stores.
Learn how contextual compression techniques strip irrelevant information from retrieved chunks before they reach the LLM, improving both answer quality and token efficiency.
Learn the parent-child chunking strategy where small chunks provide precise search matches while their larger parent chunks provide the full context needed for accurate generation.
Learn how to build hybrid RAG systems that combine document retrieval with SQL database queries and API calls, unifying structured and unstructured data in a single pipeline.
Learn how Self-RAG enables language models to decide when to retrieve, evaluate their own outputs for relevance and support, and regenerate when quality is insufficient. Full implementation guide.
Learn practical techniques to dramatically reduce RAG pipeline latency including async retrieval, semantic caching, pre-computation, and embedding optimization without sacrificing answer quality.
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