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.
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—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.
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.
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 grounds LLMs with mainframe docs for accurate answers like CICS errors; agents automate tasks like health checks and tickets, boosting productivity amid staff shortages.
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 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.
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 retrieves semantically similar but irrelevant text chunks, yielding high-confidence wrong answers that fail in production—not demos—driving 2026 shift to vectorless approaches.
Gemini's multimodal embeddings enable unified text-image retrieval for RAG, using Matryoshka reps for flexible dimensionality and cost-optimized context engineering.
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.
Claude Code + RAG progresses through 7 levels from basic auto-memory retrieval to agentic graph systems using tools like Karpathy's Obsidian, LightRAG, RAG-Anything, and Gemini Embedding 2 for production AI apps.
PageIndex creates LLM-reasoned hierarchical tree indexes from long documents for relevance-focused retrieval via tree search, hitting 98.7% accuracy on FinanceBench vs. vector RAG's similarity flaws—no DBs or chunks needed.
