We continuously share large‑model know‑how, helping you master core skills—LLM, RAG, fine‑tuning, deployment—from zero to job offer, tailored for career‑switchers, autumn recruiters, and those seeking stable large‑model positions.
This article breaks down the end‑to‑end engineering pipeline for constructing a Retrieval‑Augmented Generation (RAG) knowledge base, covering document parsing, data cleaning, semantic chunking, embedding, and index creation, plus practical optimization tips and a concise interview answer framework.
This article explains why most LLM‑based agents only perform pseudo‑planning through prompts or hard‑coded loops, outlines when to rely on prompt‑driven versus program‑driven planning, compares popular frameworks such as ReAct, MRKL, BabyAGI and AutoGPT, and clarifies what true autonomous planning would require.
This article explains the fundamental purpose, architecture, and variants of the Transformer model—including Encoder‑Decoder, Encoder‑only, and Decoder‑only designs—while detailing how attention mechanisms work and why modern large‑language models favor the Decoder‑only approach, providing a concise framework for answering interview questions.
This guide walks through the complete end‑to‑end process of training a large language model from scratch, covering data collection, cleaning, tokenization, pre‑training objectives and engineering, post‑training alignment methods, scaling laws, over‑fitting mitigation, and gradient‑stability techniques.
The author builds an AI‑driven end‑to‑end assistant that transforms a research paper into a structured reading note, generates reproducible code, runs experiments, summarizes results, and creates a report, demonstrating how large language models like Kimi K2 can streamline the entire paper‑to‑implementation workflow.
Preparing for large-model interview? This guide reveals why interviewers probe seemingly minor components—positional encoding, residual connections, layer normalization, and feed-forward networks—explains each technique's purpose, variants, and how to answer confidently, plus practical tips and a learning roadmap to boost your chances.
This guide breaks down the core logic, computation steps, formulas, and common interview questions about Self‑Attention and Multi‑Head Attention in Transformers, offering concrete explanations, dimensional examples, and practical answering techniques to help candidates ace large‑model algorithm interviews.
This article explains why full fine‑tuning of large models is impractical, introduces parameter‑efficient fine‑tuning (PEFT) with LoRA and QLoRA, provides mathematical foundations, implementation code, resource‑usage analysis, interview question templates, and practical deployment tips for real‑world AI projects.
This guide explains why RAG systems often produce off‑topic answers, outlines how to measure hit‑rate, retrieval, reranking and generation metrics, provides step‑by‑step evaluation pipelines, code examples, real‑world case studies, and interview‑ready templates for diagnosing and optimizing each stage of the pipeline.
This comprehensive guide explains the full RLHF training pipeline, the mathematical foundations of reward modeling and PPO, and introduces DPO and KTO algorithms—including their implementations, advantages, limitations, and practical tuning strategies—for building aligned large language models.
