AI Backend Engineering
Focused ai backend engineering articles with clear context, practical examples, source links where needed, and honest limits.
10 articles in this section.
Rate limit expensive AI endpoints by user and workload
Request count alone is a weak limit for AI because two calls can differ by thousands of tokens and minutes of tool execution.
Redact PII from AI logs without losing debugging context
AI logs can contain names, account data, documents, and secrets, so observability must capture decisions and timings without copying raw private content.
Design an AI evaluation set that catches regressions
A useful evaluation set contains real failure cases, expected properties, and stable scoring rules rather than a few prompts that always look impressive.
Version embeddings without breaking vector search
Changing an embedding model changes vector meaning, so old and new vectors should never be mixed in one unexplained similarity space.
Build an AI model fallback without hiding quality failures
A fallback model can improve availability only when the application records why routing changed and verifies that the cheaper model can satisfy the same contract.
Set a token and cost budget for every AI feature
A production AI endpoint needs a per-request ceiling and a monthly feature budget before higher traffic turns a useful prototype into an uncontrolled bill.
Retry LLM requests without creating a retry storm
Retries should cover transient failures only, use exponential backoff with jitter, and stop inside a total time budget.
Stream AI responses with SSE without leaking abandoned work
Streaming feels fast, but the server must cancel upstream generation when the browser disconnects and must not keep billing for an abandoned response.
Validate LLM structured output with Zod before using it
Schema-constrained output improves shape, but the application must still validate values and reject impossible combinations before they reach business logic.
Idempotent AI tool calls in Node.js: prevent duplicate side effects
An AI agent may retry a tool after a timeout even when the first call succeeded, so every money-moving or state-changing tool needs a stable operation key.