Embedding Index Lifecycle Gates for RAG Systems
Treat RAG embedding indexes as deployable artifacts with current and candidate manifests, recall regression checks, metadata validation, snapshots, restore drills, lineage, and promotion evidence.
Technical library
Articles
Long-form AI engineering articles for systems, workflows, architecture, and production decisions.
11published articles
Sep 19, 2026latest update
Browse by topic
Choose a topic to focus the library.
Retrieval & Context
11 articles found
Retrieval & Context
11 articlesPoint-in-Time Feature Stores for Hybrid AI Decisions
Build a local Feast feature store with point-in-time training retrieval, SQLite online serving, freshness gates, and deterministic scoring for hybrid AI decisions.
AI Data Contracts for RAG and Agent Context
Build a .NET data-contract pipeline that admits only fresh, owned, audience-safe, lineage-backed insurance policy documents into RAG or agent context.
Session Memory and Context Compaction for Production AI Agents
Treat long-running agent context as a controlled system. Pin required facts, compact old history, expire stale notes, and carry forward only what the next turn is allowed to rely on.
Production Memory Architecture for AI Systems
Build a local-first memory architecture with deterministic write guardrails, bounded retrieval, context budgeting, and evidence-only answers.
Deterministic Context Budgeting for Reliable LLM Prompting
Build a deterministic context budgeting pipeline that prioritizes required evidence, enforces token limits, and composes inspectable prompts.
Deterministic Semantic Retrieval with Embeddings and Vector Search
Learn the core AI pattern for semantic retrieval with embeddings as representation, deterministic vector ranking, thresholded control, and optional pgvector scaling.
Building a Local Semantic Runbook Search Engine with Ollama and Microsoft.Extensions.AI
Build a local semantic search engine for engineering runbooks using Ollama and Microsoft.Extensions.AI for deterministic retrieval.
Context Engineering Fundamentals
Designing, structuring, and managing context to deliver personalized, consistent, memory-aware AI across sessions and complex workflows.
Category-Aware Local RAG System using ASP.NET Core MVC, Ollama, and pgvector
Build a local, category-aware RAG system with ASP.NET Core MVC, Ollama LLMs, and PostgreSQL + pgvector for secure, context-bound enterprise Q&A.
Local RAG System using Semantic Kernel, Ollama, and Qdrant
Build a C# console-based Retrieval-Augmented Generation (RAG) system leveraging Semantic Kernel, Ollama LLMs, and QDrant vector search for context-aware document Q&A.