{"product_id":"prompt-engineering-for-llms","title":"Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications by John Berryman, Albert Ziegler","description":"\u003ch2\u003ePrompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications by John Berryman, Albert Ziegler\u003c\/h2\u003e\n\u003cp\u003eThe core philosophy of \u003ci data-path-to-node=\"7\" data-index-in-node=\"23\"\u003ePrompt Engineering for LLMs\u003c\/i\u003e is that \u003cb data-path-to-node=\"7\" data-index-in-node=\"59\"\u003elarge language models do not understand human intent; they are simply hyper-advanced text-completion engines.\u003c\/b\u003e Amateurs talk to an LLM like a human assistant and get unpredictable results. \u003cspan class=\"citation-163 citation-end-163\"\u003eProfessionals realize that an LLM acts by picking up patterns in its training data.\u003csup class=\"superscript\" data-turn-source-index=\"3\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e By designing input contexts that mimic highly structured, authoritative formats, engineers can control model weights to output consistent, deterministic code, math, or JSON.\u003cspan class=\"citation-162 citation-end-162\"\u003eThe book is uniquely valuable because it approaches prompt crafting from an application-layer perspective.\u003csup class=\"superscript\" data-turn-source-index=\"4\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e The authors introduce the \u003cb data-path-to-node=\"8\" data-index-in-node=\"133\"\u003e\"LLM Loop\" framework\u003c\/b\u003e, teaching developers how to turn complex user problems into dynamic prompts, execute them via APIs, and then programmatically parse those responses back into clean software variables. From managing context windows and mitigating token bloat to taming temperature and measuring precise application performance, this text acts as a complete system design manual for generative AI backends.\u003c\/p\u003e\n\u003cp data-path-to-node=\"30\"\u003eAs tech companies rush to build generative AI features into their software stacks, teams consistently hit the same wall: their code works fine during local testing, but falls apart in production due to random hallucinations, API latency spikes, or unexpected token costs.\u003c\/p\u003e\n\u003cp data-path-to-node=\"31\" id=\"p-rc_9a675919487f1d86-103\"\u003e\u003ci data-path-to-node=\"31\" data-index-in-node=\"0\"\u003ePrompt Engineering for LLMs\u003c\/i\u003e is an incredibly important text because it completely strips away the hype and treats AI generation as a rigorous system design problem. \u003cspan class=\"citation-156 citation-end-156\"\u003eBecause Berryman and Ziegler spent years architecting GitHub Copilot from the ground up, they bypass basic beginner material to address real-world, production-scale bottlenecks.\u003csup class=\"superscript\" data-turn-source-index=\"10\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e \u003cspan class=\"citation-155 citation-end-155\"\u003eBy teaching developers how to handle raw context triage, exploit transformer mechanics, and construct programmatic evaluation loops, this book ensures your engineering team can transition erratic AI behavior into a reliable, enterprise-grade software asset.\u003csup class=\"superscript\" data-turn-source-index=\"11\"\u003e\u003c!----\u003e\u003c\/sup\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eLanguage: English.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eGenre: Applied Artificial Intelligence.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eBinding: সেলাই করা বাইন্ডিং\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eQuality: Premium Quality Books.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePrinting: High Quality Printing.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003ePaper: Eye Friendly paper (Cream White)\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cstrong\u003eCover: Matt cover (Paperback).\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e","brand":"Royal Books BD","offers":[{"title":"Default Title","offer_id":47234651193529,"sku":null,"price":280.0,"currency_code":"BDT","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0780\/0874\/6169\/files\/Prompt_Engineering_for_LLMs.jpg?v=1779388314","url":"https:\/\/royalbooksbd.com\/products\/prompt-engineering-for-llms","provider":"Royal Books BD","version":"1.0","type":"link"}