16 min readApplied
Long Context Won't Replace RAG Retrieval
Long context windows promise the end of RAG. They don't deliver it. Here's what long context actually fixes, what it silently can't, and how to decide.
Retrieval & RAG Architecture
How retrieval systems are designed, and how to choose your stack.
11 posts
9 min readDeep dive
Inherent vs pgvector: Extension You Run or Pipeline You Buy?
Inherent vs pgvector, compared fairly: pgvector is a Postgres extension for vector search; Inherent is the managed context pipeline around it — ingestion, retrieval, audit.
7 min readApplied
Inherent vs Pinecone: Vector Database or Context Layer?
Inherent vs Pinecone, compared fairly: Pinecone is a managed vector database; Inherent is managed context infrastructure — ingestion, retrieval, audit.
7 min readPrimer
Vector Search and Vector Database Basics for CEOs
A CEO guide to vector search and vector databases: embeddings, similarity, filters, hybrid search, and the trust controls AI retrieval needs.
8 min readPrimer
Semantic Search for CEOs: When AI Search Can Be Trusted
A CEO guide to semantic search and AI search: when meaning-based retrieval helps, when keyword or hybrid search is safer, and what trust controls matter.
7 min readPrimer
RAG Pipeline: A CEO Guide to Reliable AI Answers
A CEO guide to the RAG pipeline: how source truth, ingestion, retrieval, freshness, permissions, and evidence decide whether AI answers can be trusted.
8 min readApplied
RAG vs MCP: A Practical Guide
A practical guide to RAG vs MCP: when to use native search, Google Drive MCP, Confluence MCP, a RAG system, or a layered approach.
6 min readPrimer
RAG Architecture Tradeoffs in Plain English
A CEO-level guide to RAG architecture tradeoffs: speed, freshness, traceability, cost, permissions, and when retrieval is ready for production.
6 min readPrimer
RAG for CEOs
Retrieval augmented generation helps AI use company context, but RAG alone does not guarantee freshness, permissions, business rules, or proof.
9 min readDeep dive
RAG Ingestion Pipeline: A Field Guide for FDEs [2026]
A field guide for forward deployed engineers building RAG ingestion pipelines: freshness, permissions, versioning, and retrieval receipts.
6 min readApplied
Production RAG Needs Truth and Memory
Production RAG needs more than retrieval. Learn how source truth, scoped AI memory, permissions, and audit receipts make RAG systems reliable.
Inherent on Substack
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