Question 1

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 2

What is prompt injection and how do you defend against it?

Accepted Answer

**Target Audience:** Engineers building LLM features that read outside content (web pages, emails, documents, tool results) or take actions, and anyone asked to explain why "just tell the model to ignore it" is not a defence.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (both examples below, identical output). Research and standards cited were checked by web search in October 2026: OWASP Top 10 for LLM Applications 2025 (via reference summaries), the OpenAI instruction

Question 3

What is red-teaming an LLM application and how is it done?

Accepted Answer

**Target Audience:** Engineers and tech leads asked to "red-team the assistant before launch" who want a method rather than a list of famous jailbreak prompts.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the example below, identical output). Research, tools and standards mentioned were checked by web search in October 2026: Anthropic's Constitutional Classifiers announcement (February 2025), the Crescendo paper (Russinovich, Salem and Eldan; USENIX Security 2025), sec

AI ENGINEERINGDECODED.

Hard

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

What is prompt injection and how do you defend against it?

What is red-teaming an LLM application and how is it done?