Master retrieval-augmented generation, vector databases, embeddings, and semantic search systems.
Qdrant delivers 2x lower latency at half the cost, but Pinecone ships in days with zero ops. We tested both in production—here's which fits your team.
Cross-encoder reranking boosted our client's RAG accuracy from 73% to 91%—but added 300ms that killed another's chatbot. Here's how to decide.
Weaviate's free sandbox lasts 14 days. We break down Flex ($45/mo), Premium ($400/mo), self-hosted costs, and when each tier actually makes financial sense.
We built a GraphRAG system with Neo4j for a 14-source enterprise platform. Here's how entity extraction, graph modeling, and query routing work at scale.
Most teams measure RAG success with vibes, not metrics. Learn the specific evaluation approaches that reveal whether your retrieval pipeline delivers accurate, relevant results.
384, 768, 1024, or 3072 dimensions? The right choice depends on your data complexity, latency requirements, and storage budget—not the highest number available.
Pinecone starts at $70/mo, Weaviate and Qdrant are free to self-host. We tested all three in production—here's where each wins on latency, cost, and scaling.
Context loss during document chunking kills RAG accuracy. Learn the semantic chunking strategies and overlap techniques that preserve meaning while optimizing retrieval performance.
Real-world framework for choosing embedding models based on production requirements, cost, and performance—not just benchmarks. From 20+ RAG implementations.