KyroDB Learn
Field guides for the context failures production AI actually hits.
Concise, source-backed guides with precise definitions, failure symptoms, how KyroDB solves it, implementation notes, and links into the runtime docs.
Context Correctness for AI Agents
Context correctness is the runtime discipline of making sure retrieved AI-agent context is fresh, scoped, traceable, and safe before it reaches a model prompt.
Fresh Context for AI Agents
Fresh AI-agent context requires source ownership, invalidation, scoped watermarks, and fail-closed retrieval when freshness cannot be proven.
RAG Freshness
RAG freshness is the guarantee that retrieved context reflects the current source state for the requested scope before a model uses it.
Stale Context Prevention
Prevent stale context by making freshness, invalidation, source generations, and strict failure behavior part of the runtime retrieval contract.
Context Rot
Context rot is the gradual decay of AI-agent context quality as source knowledge changes, memory accumulates, and old retrieval artifacts keep entering prompts.
Context Pollution
Context pollution is the failure mode where irrelevant, stale, conflicting, low-quality, or unauthorized material contaminates an AI prompt.
AI Memory Correctness
AI memory correctness means remembered or retrieved information is current, scoped, non-polluting, and explainable before it influences an agent.
RAG Hallucination Prevention
RAG hallucination prevention starts by making sure the model is grounded in fresh, scoped, provable context instead of stale or polluted retrieval.
Scope-Safe Retrieval
Scope-safe retrieval ensures AI agents only retrieve and reuse context within the correct tenant, namespace, authorization, policy, and filter boundary.
Context Provenance
Context provenance records where retrieved context came from, why it was included, what was omitted, and what evidence supported the retrieval decision.
Context Proof
A context proof is runtime evidence that retrieved context was fresh, scoped, and safe enough to serve, or that it was correctly degraded or blocked.
Vector Database Context Layer
AI agents need a context correctness layer above vector databases to enforce freshness, scope, provenance, and proof around retrieval.