Hybrid Search Blueprint Series: Semantic Boosting
RAG Series (15): CRAG — Self-Correcting When Retrieval Falls Short
Building Production RAG: From 52% to 89% Accuracy with a 6-Stage Pipeline
Fully open-source RAG with pgvector + pgai + Ollama, and ragvitals watching for drift
RAG Series (13): Query Optimization — Asking Better Questions
Beyond Vector Search: Mastering Contextual Retrieval for LLMs
Gemini API File Search ahora es multimodal con metadata y citas por página
How We Slashed RAG Eval Costs by 94% and Caught 99.8% of Hallucinations Using Adaptive Tri-Vector Evaluation
Current Situation Analysis At FAANG scale, RAG evaluation is not a "nice-to-have"; it's the gatekeeper of production stability. When our team first adopted RAG for the internal knowledge assistant serving 40,000 engineers, we followed the standard playbook: generate a golden dataset, run RAGAS metr...
Cutting Multi-Document RAG Latency by 81% and Cost by 60% with Hierarchical Chunk Routing
Current Situation Analysis Multi-document RAG breaks in production when you cross the 10,000-document threshold. Tutorials teach you to load everything into a single vector store, run similarity_search(k=10), and concatenate the results. This works for proof-of-concepts.
Slashing RAG Costs by 64% and Latency to 180ms with Semantic Caching and Adaptive Chunking
Current Situation Analysis When we audited our internal RAG pipelines across three product lines, the results were embarrassing. We were burning $14,000/month in LLM inference costs for a system with 42% cacheable query overlap.
How I Cut Knowledge Base Indexing Costs by 78% and Latency to 12ms with Query-Adaptive Routing
Current Situation Analysis Enterprise knowledge bases don't fail because they lack data. They fail because they treat heterogeneous queries as homogeneous workloads.
How We Cut Multi-Document RAG Latency by 68% and Token Costs by 41% with Intent-Guided Context Fusion
Current Situation Analysis Multi-document RAG is broken in production. Not because retrieval fails, but because context assembly fails. Most engineering teams treat multi-document retrieval as a volume problem: ingest more PDFs, increase chunk count, raise top-k, and pray the LLM synthesizes correc...
Automating RAG Evaluation: Cutting Hallucination by 94% and Eval Costs by 65% with Delta-Weighted Scoring
Current Situation Analysis Most engineering teams treat RAG evaluation as a batch analytics task. You spin up RAGAS or LangSmith, run a dataset of 500 queries once a week, and stare at a dashboard that says "Context Precision: 0.82". This approach fails in production for three reasons: 1.
Production KB Indexing: 12ms P99, 62% Cost Reduction, and the Metadata-First Pruning Pattern
Current Situation Analysis Most knowledge base indexing tutorials stop at split_text and vector_search. They show you how to dump chunks into Pinecone or pgvector and query with cosine similarity. This works for a 500-document demo.
Cutting RAG Inference Costs by 62% and Hallucinations by 89% with Pre-LLM Retrieval Quality Scoring and Tiered Routing
Current Situation Analysis When I joined the AI infrastructure team at our FAANG-scale organization, our RAG pipeline was bleeding money and trust. We were processing 1.2M queries daily.
RAG Evaluation Metrics: Engineering Reliable Retrieval-Augmented Generation
# RAG Evaluation Metrics: Engineering Reliable Retrieval-Augmented Generation ## Current Situation Analysis Retrieval-Augmented Generation (RAG) has shifted from experimental prototype to production i
Knowledge Base Indexing: Engineering Reliable Retrieval at Scale
# Knowledge Base Indexing: Engineering Reliable Retrieval at Scale ## Current Situation Analysis Knowledge base indexing has transitioned from a peripheral search concern to a critical infrastructure
Enterprise RAG Architecture: Production-Grade Design Patterns
# Enterprise RAG Architecture: Production-Grade Design Patterns ## Current Situation Analysis The gap between prototype RAG and production RAG is widening. While tutorial ecosystems have successfully
Multi-Document RAG: Architecture, Implementation, and Production Hardening
# Multi-Document RAG: Architecture, Implementation, and Production Hardening ## Current Situation Analysis Enterprise knowledge retrieval is inherently multi-document. Legal researchers cross-referenc
Cutting Multi-Document RAG Latency by 68% and Hallucinations by 42% with Graph-Aware Aggregation
Current Situation Analysis Most engineering teams implement multi-document RAG by treating every document as a bag of independent chunks. You ingest PDFs, split by token count, embed everything, and retrieve the top-k chunks based on cosine similarity.
Cutting RAG Eval Costs by 82%: A Tiered Pipeline with Semantic Caching and Dynamic Thresholds
Current Situation Analysis RAG evaluation is the silent cost center in production AI. Most teams treat evaluation as a batch benchmark: run RAGAS 0.2.1 or LangSmith against a static dataset, collect faithfulness and answer relevance scores, and ship. This works for 50 examples.
Cut Indexing Latency by 85% and Vector Costs by 62% Using Recursive Semantic Chunking and RRF Hybrid Search
Current Situation Analysis When we migrated our internal knowledge base to an LLM-driven architecture, our initial indexing pipeline looked like every tutorial on the internet: split text into fixed 512-token chunks, call the embedding API, and dump vectors into Pinecone.
Cutting RAG Pipeline Latency by 68% and Reducing Vector DB Costs by $12k/Month: A Production-Ready Architecture
Current Situation Analysis Most engineering teams treat Retrieval-Augmented Generation (RAG) as a single retrieval step: chunk text, embed it, store in a vector database, and run similarity search.
Small-to-Big RAG: Your AI Needs a Better Context 🧠
Architecting Grounded AI: A Production-Ready Retrieval Pipeline
pgvector with Node.js: Build Semantic Search on PostgreSQL
Day 11: Conversational RAG — How to Chat with Your Documents 💬
Orchestrating Grounded Intelligence: The RAG Retrieval-Generation Pipeline
Day 9: RAG — Giving Your AI a Private Library 📚
Vector Databases for AI: Pinecone vs Weaviate vs pgvector
Vector databases compared: Pinecone, Weaviate, pgvector, Qdrant, Milvus.