Prompt injection in action-taking AI agents is an authorization problem. Learn how to enforce tool permissions, scope credentials, require approvals, and limit blast radius.
Interview Preparation
Behavioral and technical interview prep for software engineers ready to take their careers to the next level.
118 posts
An LLM judge can help evaluate production AI output, but its scores need validation. Learn when to use deterministic checks, how to sample and calibrate judgments, and why judge changes require an overlap period.
A fast, well-formed LLM response can still be wrong. Learn how traces, offline evaluation, and sampled online scoring expose failures that latency and error-rate dashboards miss.
MCP is a boundary to design against, not a framework to adopt. Learn how tools, resources, authorization, and deployment choices shape reliable AI-agent integrations.
Start with one agent, then split only when tool overload, context exhaustion, or parallel subtasks deliver a measurable benefit over the coordination, latency, and cost.
Prompt prefix caching reuses identical leading tokens; semantic response caching can substitute an answer for a merely similar question. Learn the validity checks, cost model, and routing tradeoffs behind each.
LLM inference splits into compute-heavy prefill and memory-bandwidth-sensitive decode. Learn why batching helps, how long prompts disrupt streaming, and which latency metrics to measure.
In a vector database system design interview, focus on chunk policy, metadata, filtered search, and rebuild strategy—not just the store you name.
Learn how to design a RAG system for interviews by separating retrieval from generation, with distinct freshness, latency, failure, and quality decisions.
When an AI assistant can write working code in seconds, interviewers stop testing typing speed and start testing whether you can clarify requirements, own the design, and verify what the model produced.
