#rag
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RAG Evolves from Keyword Search to Agentic Reasoning
Information retrieval progressed from keyword matching (TF-IDF/BM25) to semantic vectors, hybrid systems, RAG for LLM augmentation, and agentic setups that autonomously plan retrieval, validate sources, and synthesize multi-step answers.
IBM TechnologyGraphRAG and Vectorless RAG Fix Vector RAG's Silent Failures
Vector RAG structurally fails by confidently hallucinating on semantically similar but incorrect chunks with no errors logged. GraphRAG maps entity relationships via graphs; Vectorless RAG skips vectors for LLM reasoning over document structure—each excels where the other can't.
Context Engineering Unlocks AI via RAG & GraphRAG
Context—not model intelligence—is AI's main bottleneck. Build contextual systems with connected access, knowledge layers, precision retrieval (agentic RAG, GraphRAG, compression), and runtime governance for relevant, governed outputs.
IBM TechnologyPageIndex: Vectorless RAG via LLM Tree Reasoning
PageIndex builds hierarchical document trees with section summaries, enabling LLMs to reason over structure for precise retrieval without embeddings—boosting accuracy on complex docs like FinanceBench.
Phi-4-Mini Masterclass: Quantized LLM Pipelines
Build end-to-end Phi-4-mini workflows in Colab: 4-bit inference, streaming chat, CoT reasoning, tool calling, RAG, and LoRA fine-tuning—all in one notebook with full code.
RAG and Agents Fix LLM Flaws in Mainframe Ops
RAG grounds LLMs with mainframe docs for accurate answers like CICS errors; agents automate tasks like health checks and tickets, boosting productivity amid staff shortages.
IBM TechnologyRAG Grounds LLMs, Agents Automate Mainframe Ops
RAG ingests mainframe docs to fix LLM inaccuracies like wrong CICS error diagnosis; agents automate tasks like health checks and ticketing for trusted productivity in hybrid clouds.
rag-injection-scanner Detects Hidden RAG Prompt Attacks
rag-injection-scanner uses layered regex, NLP heuristics, and LLM judging with XML isolation to detect indirect prompt injections in RAG documents pre-ingestion, catching 3/3 tested attacks across 42 chunks with 0 false positives and 89% avoiding LLM calls.
PageIndex: LLM Reasoning Beats Vector RAG on Structured Docs
Replace vector databases with PageIndex's hierarchical tree index for RAG: LLM reasons through document structure to retrieve exact answers, hitting 98.7% accuracy on FinanceBench vs. traditional vector RAG's 50%. Ideal for long docs like 10-K filings.
Vector RAG's Semantic Trap: Wrong Chunks, Confident Errors
Vector RAG retrieves semantically similar but irrelevant text chunks, yielding high-confidence wrong answers that fail in production—not demos—driving 2026 shift to vectorless approaches.
Google Embeddings 2: Multimodal RAG Revolution
Gemini's multimodal embeddings enable unified text-image retrieval for RAG, using Matryoshka reps for flexible dimensionality and cost-optimized context engineering.
20B Chroma Context-1 Fixes RAG Retrieval Woes
Replace frontier models in RAG retrieval with Chroma Context-1, a 20B specialist that beats them at search, cutting costs from $0.12/query and latency from 15s.
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