AI Engineering
Building AI systems that survive contact with real traffic.
What does ai engineering cover?
The implementation layer: retrieval, evaluation, orchestration, observability and production reliability.
Definitions in this cluster
Retrieval-augmented generation
Retrieval-augmented generation is a pattern where relevant documents are fetched from a knowledge base at query time and inserted into the model's prompt, so answers are grounded in your own content rather than the model's training data.
Vector database
A vector database stores text (or images) as numeric embeddings and retrieves items by semantic similarity rather than keyword match. It is the retrieval layer behind most production RAG systems.
Embedding
An embedding is a list of numbers representing the meaning of a piece of text, produced by a model so that semantically similar texts sit close together in that numeric space.
Prompt engineering
Prompt engineering is the practice of structuring the instructions, context and examples given to a language model so it produces reliable, correctly formatted output for a specific task.
Orchestration
Orchestration is the coordination layer that decides which model, tool or sub-process handles each step of a task, manages retries and failures, and keeps state across the sequence.
Evaluation
Evaluation is the systematic scoring of AI output against a fixed set of test cases with known good answers. It is what turns 'the demo felt good' into a measurable quality baseline you can defend changes against.
Guides & analysis
Retrieval-Augmented Generation in Production: What Actually Breaks
The failure modes of production RAG — chunking, retrieval quality, freshness, evaluation — and how to design around them.