Share Agent interview questions and standard answers, offering a one‑stop solution for Agent interviews, backed by senior AI Agent developers from leading tech firms.
The article defines tokens (now officially called “词元”), explains why large language models require numeric input, and details three main tokenization strategies—word‑based, character‑based, and subword—along with the sub‑methods BPE, WordPiece, and Unigram, highlighting their advantages and drawbacks.
The article explains how LLM‑based agents implement reflection by first generating output, then evaluating it either through self‑feedback or by invoking external tools, and finally correcting the result, detailing two self‑feedback methods and typical external‑feedback scenarios.
The article explains how to improve Retrieval‑Augmented Generation (RAG) systems by interpreting three key metrics—context recall, context precision, and answer correctness—and provides concrete step‑by‑step actions such as checking the knowledge base, upgrading embedding models, rewriting queries, adding a rerank model, and refining prompts and generation parameters.
The article explains how to assess Retrieval-Augmented Generation (RAG) projects using the Ragas automated evaluation framework, detailing four key dimensions—recall quality, answer faithfulness, answer relevance, and context utilization—and describes the underlying metrics for both retrieval and generation stages.
The article explains ReAct—a Reason‑Act framework for large language model agents that observes, reasons, takes actions via tools, receives feedback, and iterates—highlighting its distinction from plain QA, its step‑by‑step workflow, practical importance, and a weather‑query example.
The article explains that multi-agent systems typically operate in three patterns—sequential execution, parallel execution, and an evaluator-optimizer loop—covers when each pattern is appropriate, and offers interview tips on how to discuss these designs effectively.
The article defines Skills as structured local folders that encapsulate domain‑specific processes, knowledge, and tools for large language models, contrasts them with temporary Prompts, outlines suitable use cases, details their components, and explains their on‑demand loading mechanism that saves tokens.
Retrieval‑Augmented Generation (RAG) lets large language models first fetch relevant documents and then generate answers, addressing the inability of models to answer private or domain‑specific queries by precisely feeding them the most pertinent knowledge.
The author, a former backend engineer turned AI Agent developer, explains how LLM uncertainty, context engineering, shifting code responsibilities, workflow standards, new failure modes, and the ReAct paradigm shape modern Agent development, and outlines tasks best suited—or unsuited—for LLMs.
The article outlines a systematic Prompt Engineering process that starts with defining task goals and metrics, structures prompts into modular components, uses offline evaluation and bad‑case analysis, incorporates RAG or tools when needed, and continuously monitors accuracy, hallucination, latency and cost.
