AI Native Digest is a technical knowledge base and blog covering how AI-native applications are actually built: vector databases, retrieval and RAG, agent architectures, embeddings, evaluation, and the infrastructure that runs production AI systems. Every article works through the underlying mechanics — not marketing framing — so engineers can understand how a system behaves and why.
A knowledge graph is a way of representing information as a network of real-world entities — people, places, concepts, events — connected by explicit relationships, so that both what a piece of data is and how it relates to everything else are captured together, rather than the relationships being left implicit or reconstructed later. Concretely, […]
Product quantization (PQ) is a technique for compressing high-dimensional vectors by splitting each vector into smaller segments, replacing each segment with the ID of its closest match from a small, pre-trained set of representative values, and storing only those IDs instead of the original numbers. It’s a lossy compression method, meaning you permanently lose some […]
HyDE (Hypothetical Document Embeddings) is a retrieval technique that improves search accuracy by having a language model first generate a fake, “hypothetical” answer to a query, embedding that fake answer instead of the query itself, and then searching a real document collection for whatever is most similar to that embedding. Counterintuitively, the fake document doesn’t […]
Procedural memory is memory for how to do something — a learned skill, routine, or sequence of steps — as opposed to memory for facts (semantic memory) or memory for specific past events (episodic memory). The defining trait of procedural memory is that it’s implicit: you can ride a bike or type on a keyboard […]
Semantic memory is memory for general facts and knowledge that hold true independent of any specific moment — like knowing that Paris is the capital of France — while episodic memory is memory for specific events tied to a particular time and context, like remembering a conversation you had yesterday. The core difference is that […]
A relational database is a system for storing data as a set of tables — rows and columns, like structured spreadsheets — where the relationships between different tables are defined explicitly through shared key values, and a standardized query language (SQL) is used to read, write, and combine that data. The “relational” in the name […]
AI model access controls work by treating every entity that can call a model or touch the data behind it — whether that’s a human user, an application, or an autonomous AI agent — as an identity with its own permissions, then enforcing those permissions independently of the model itself at every step: authenticating who […]
Document filtering using field mappings works by declaring which fields in your documents (metadata like category, date, status, price, or ID) are mapped to structured, indexed properties at the time your data is stored, so that a search system can build a fast lookup — typically an inverted index — over those fields and use […]
Domain-specific embeddings improve semantic search by encoding the specialized vocabulary, terminology, and conceptual relationships of a particular field — like legal contracts, medical literature, or codebases — more accurately than a general-purpose embedding model can, because the general model was never trained on enough of that specific language to represent it precisely in vector space. […]
You deploy a database-backed AI application on Kubernetes by packaging the database as a StatefulSet (or, more practically, via its official Helm chart) with persistent volumes for its data, running it as a multi-node cluster for high availability, and then connecting your application pods to it over an internal Kubernetes service — while separately configuring […]