{"id":992306,"date":"2026-10-10T13:22:11","date_gmt":"2026-10-10T06:22:11","guid":{"rendered":"https:\/\/5id.vn\/gen-ai-cheatsheet-20-essential-concepts-for-practitioners\/"},"modified":"2026-10-10T13:22:11","modified_gmt":"2026-10-10T06:22:11","slug":"gen-ai-cheatsheet-20-essential-concepts-for-practitioners","status":"publish","type":"post","link":"https:\/\/5id.vn\/vi\/gen-ai-cheatsheet-20-essential-concepts-for-practitioners\/","title":{"rendered":"T\u00f3m t\u1eaft ki\u1ebfn th\u1ee9c c\u01a1 b\u1ea3n v\u1ec1 Tr\u00ed tu\u1ec7 Nh\u00e2n t\u1ea1o th\u1ebf h\u1ec7 m\u1edbi: 20 kh\u00e1i ni\u1ec7m thi\u1ebft y\u1ebfu d\u00e0nh cho ng\u01b0\u1eddi th\u1ef1c h\u00e0nh"},"content":{"rendered":"<div class=\"vgblk-rw-wrapper limit-wrapper\">\n<div class=\"seo-article-body\" style=\"width:100%;max-width:830px;margin:0 auto;line-height:1.7;box-sizing:border-box\">\n<p>If you&#39;re diving into generative AI and feel overwhelmed by a flood of terms such as tokenization, embeddings, RAG, fine-tuning, or agents, the <strong>Gen AI cheatsheet<\/strong> offers a concise roadmap. This guide gathers twenty core ideas, starting with foundational pieces like tokenization, embeddings, and attention, and advancing to sophisticated topics such as context engineering, retrieval-augmented generation (RAG), hallucination mitigation, tool usage, agent architecture, and systematic evaluation.<\/p>\n<h2>Key Highlights<\/h2>\n<ul>\n<li><strong>Tokenization fundamentals<\/strong> &#8211; Understand how text is broken into tokens and why large language models operate on tokens rather than raw characters.<\/li>\n<li><strong>Fine-tuning vs. LoRA<\/strong> &#8211; Learn the practical differences and decide which technique fits a given scenario.<\/li>\n<li><strong>RAG mechanics<\/strong> &#8211; Discover that poor RAG performance usually stems from weak retrieval components, not from the generative model itself.<\/li>\n<li><strong>Agent design<\/strong> &#8211; Realize that an agent is essentially a loop that can invoke functions, enabling dynamic tool use.<\/li>\n<li><strong>Evaluating non-deterministic AI<\/strong> &#8211; Follow a structured approach to assess systems whose outputs vary across runs.<\/li>\n<li><strong>Broader concept coverage<\/strong> &#8211; From embeddings and attention to hallucination handling and evaluation frameworks, the cheatsheet spans the entire spectrum of modern generative AI.<\/li>\n<\/ul>\n<p>The resource is especially useful for anyone preparing for interviews, seeking to systematize fragmented knowledge, or simply wanting a deeper grasp of how generative AI systems operate in practice. It condenses the most confusing jargon into clear, actionable explanations.<\/p>\n<p>&gt; Note: The author employed AI to translate the original material into Vietnamese, so some nuances or visual elements may differ from the source. If you are comfortable with English, you can view the original post at the link provided below.<\/p>\n<h2>Ph\u1ea7n k\u1ebft lu\u1eadn<\/h2>\n<p>The <strong>Gen AI cheatsheet<\/strong> serves as a practical reference that demystifies twenty pivotal concepts, helping professionals and learners alike navigate the rapidly evolving landscape of generative AI with confidence.<\/p>\n<\/div>\n<\/div>\n<p><!-- .vgblk-rw-wrapper --><\/p>","protected":false},"excerpt":{"rendered":"<p>Kh\u00e1m ph\u00e1 c\u1ea9m nang Gen AI t\u1ed5ng h\u1ee3p 20 kh\u00e1i ni\u1ec7m thi\u1ebft y\u1ebfu - t\u1eeb m\u00e3 h\u00f3a token v\u00e0 nh\u00fang embedding \u0111\u1ebfn RAG v\u00e0 ki\u1ebfn tr\u00fac t\u00e1c nh\u00e2n - ho\u00e0n h\u1ea3o<\/p>","protected":false},"author":26,"featured_media":992305,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_focuskw":"Gen AI cheatsheet","_yoast_wpseo_title":"","_yoast_wpseo_metadesc":"Explore the Gen AI cheatsheet that compiles 20 essential concepts-from tokenization and embeddings to RAG and agent architecture-perfect","_yoast_wpseo_linkdex":"90","_yoast_wpseo_content_score":"90","rank_math_focus_keyword":"","rank_math_description":"","rank_math_title":"","source_url":"","source_name":"Staff-AI","footnotes":""},"categories":[17,19,11],"tags":[],"class_list":["post-992306","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-development","category-it","category-news"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.5 (Yoast SEO v27.5) - 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