#rag
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Stop Blaming Your RAG Pipeline: 16 Production Techniques
Most RAG failures are pipeline issues, not model limitations. Improving retrieval precision through hybrid search, reranking, and rigorous evaluation is more effective than simply swapping models.
Optimizing RAG Retrieval with Hierarchical Search
Hierarchical RAG improves precision and reduces computational costs by replacing flat, corpus-wide similarity searches with a two-stage process: document-level filtering followed by targeted chunk retrieval.
DysLexLens: Analyzing Dyslexic AI User Experiences via LLMs
DysLexLens is an end-to-end framework that extracts, structures, and validates insights from noisy online forum data to understand how dyslexic learners interact with AI tools.
ToE: Hierarchical Claim Verification Against Adversarial Misinformation
Tree of Evidence (ToE) is a fact-checking framework that uses a reinforcement learning-driven agent to decompose claims into hierarchical argument trees, significantly improving verification accuracy against adversarially poisoned inputs.
DysLexLens: A Framework for Analyzing Dyslexic Learner AI Experiences
DysLexLens is an end-to-end, evidence-traceable framework that uses dictionary-driven filtering and knowledge graphs to analyze how dyslexic learners interact with AI tools via online forums.
Cross-Document AI for Predictive Financial Compliance
Moving from document-level validation to cross-document graph correlation and probabilistic risk modeling reduces false positives by 76% and enables proactive fraud detection.
AI EngineerBuilding AI-Native Search with Spanner
Google Cloud Spanner now integrates full-text, vector, and hybrid search directly into the database, eliminating the need for separate search engines, ETL pipelines, and data synchronization issues.
Google Cloud TechConfigurable Clinical Information Extraction with Agentic RAG
Agentic RAG systems for clinical data require modular configuration to balance precision and recall, as monolithic pipelines often fail to handle the high variability of medical documentation.
CONCORD: Asynchronous Sparse Aggregation for Device-Cloud RAG
CONCORD is a framework for device-cloud Retrieval-Augmented Generation that optimizes performance under document isolation by using asynchronous sparse aggregation to balance local privacy with cloud-scale retrieval.
Scaling RAG Pipelines to 10M+ Documents with High Accuracy
To minimize hallucinations at scale, implement a multi-stage RAG pipeline that combines hybrid indexing, reciprocal rank fusion, and a strict 'retrieve, constrain, verify, abstain' workflow that forces the model to cite evidence or admit ignorance.
Fixing RAG Hallucinations Through Better Retrieval Architecture
RAG failures are rarely LLM hallucinations; they are retrieval failures. To fix them, you must move beyond simple semantic search and implement robust document versioning, metadata filtering, and re-ranking.
Improving Financial Document Analysis with GraphRAG
Traditional vector-based RAG struggles with the non-linear, cross-referenced nature of financial documents. GraphRAG improves accuracy and reduces hallucinations by mapping entity relationships, ensuring multi-page data continuity.
Fixing RAG Pipelines by Optimizing Chunking, Not Models
Most RAG failures are caused by poor data retrieval, not model hallucinations. Improving chunking strategy and inspecting raw retrieved data is the most effective way to improve accuracy.
Building Stateful AI Agents with Gemini Enterprise
Google Cloud's Gemini Enterprise Agent Platform enables stateful AI agents through cloud-based sessions and automated memory banks, allowing developers to build contextual, RAG-enabled applications with minimal code.
Google Cloud TechBeyond RAG: Building Hybrid Knowledge Architectures
RAG is effective for static, unstructured retrieval but fails at reasoning, structured data, and long-term memory. Production systems require hybrid architectures that combine retrieval with knowledge graphs and persistent state.
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 TechnologyPhi-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 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.
IBM Technologyrag-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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