Question 1

How do you build resilient LLM calls with rate limits, retries and fallbacks?

Accepted Answer

**Target Audience:** Engineers putting an LLM feature in front of real users who have seen their first 429 or 529 in production and want a resilience layer that is more than "wrap it in a try/catch and try again".
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below run on a fake clock with stub providers, so the timing logic is real but no API is called). Provider behaviour (status codes, headers, SDK defaults, rate-limit algorithms) is quoted from the A

Question 2

How does streaming work in LLM APIs and why does it matter for UX?

Accepted Answer

**Target Audience:** Engineers building a chat or assistant feature who want to know what `stream: true` really changes, and what it breaks.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below, identical output). Provider event names were checked in October 2026 against the Anthropic streaming docs (platform.claude.com, "Streaming messages") and the type definitions of the official `openai` npm package (v7.36.0).

> **How to read this doc:** Every idea i

Question 3

Streaming LLM responses to the UI: SSE, chunking, abort, and resume?

Accepted Answer

**Target Audience:** Frontend and full-stack engineers wiring a streamed LLM answer into a real UI that has to survive slow networks, Stop buttons and dropped connections.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below, identical output). AI SDK details were read from the published `ai@5.0.277` and `@ai-sdk/react@2.0.280` packages and the docs shipped inside `ai@7.0.143`; OpenAI SDK details from `openai@7.36.0` type definitions; all checked in Octob

Question 4

Vercel AI SDK 5 streamText: how do you stream LLM chat to React?

Accepted Answer

**Target Audience:** React and Next.js developers building a chat page with the Vercel AI SDK 5 who want to understand what flows between `streamText` and `useChat`, not just copy the starter.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 for the protocol simulation below (identical output). AI SDK facts were read in October 2026 from the published npm packages `ai@5.0.277` and `@ai-sdk/react@2.0.280` (type definitions and source) and the migration guides shipped in `a

Question 5

Guardrails for LLM apps: how do you implement input/output safety in 2025?

Accepted Answer

**Target Audience:** Engineers who own a production LLM feature and have to make its safety checks fast, reliable and measurable, not just present.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below, identical output). Vendor facts were checked by web search in October 2026: the Amazon Bedrock user guide ("Configure streaming response behavior to filter content") and API reference (`ApplyGuardrail`), Microsoft Learn (Prompt Shields), Meta model cards (Lla

Question 6

How would you design a production RAG pipeline end-to-end in 2025-26?

Accepted Answer

**Target Audience:** Engineers preparing for an AI or backend system design round who can explain RAG in one sentence and now need to design the whole thing: ingestion, retrieval, generation, numbers, operations and evaluation.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the miniature pipeline and the sizing arithmetic below). Every input to the sizing is a stated assumption, and the prices in it are illustrative placeholders, not a vendor price list. Product facts ar

Question 7

What techniques reduce LLM latency at each stage of a request?

Accepted Answer

**Target Audience:** Engineers whose LLM feature works but feels slow, and who want a method for finding and removing latency rather than a list of tricks.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (a latency-budget model and a speculative-decoding simulation). The per-stage numbers in the budget are **stated assumptions** used for the arithmetic, not measurements of any provider; vendor claims (prompt caching latency, predicted outputs, speculative decoding speedups

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