{"product_id":"new-product-10","title":"LLM Engineering: Transformers, Agents \u0026 Production Apps","description":"\u003cp style=\"hyphenate-character: '-' !important; color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"\u003e\u003cstrong style=\"hyphenate-character: '-' !important;\"\u003eThe whole LLM pipeline, end to end — from the transformer up to a deployed app.\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e\u003cem style=\"hyphenate-character: '-' !important;\"\u003eLLM Engineering: Transformers, Agents \u0026amp; Production Apps\u003c\/em\u003e\u003cspan\u003e \u003c\/span\u003eis a comprehensive engineering reference for building with large language models the way practitioners actually do: architecture first, then the training and alignment stack, then agents, then production.\u003c\/p\u003e\n\u003cp style=\"hyphenate-character: '-' !important; color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"\u003eYou'll start inside the transformer — attention, tokenisation, and embeddings — then move through pre-training at scale, fine-tuning, and alignment (RLHF, DPO, and beyond). From there the book turns applied: prompt engineering, retrieval-augmented generation, LLM agents and multi-agent systems, long context and memory, inference optimisation and serving, and the APIs and orchestration frameworks that tie it all together. The final chapters cover evaluation and benchmarking, safety, guardrails and red-teaming, and production architecture and LLM MLOps — everything between a model and a reliable application.\u003c\/p\u003e\n\u003cp style=\"hyphenate-character: '-' !important; color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"\u003e\u003cstrong style=\"hyphenate-character: '-' !important;\"\u003eYou will learn to:\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e- Understand transformer architecture, tokenisation, and embeddings deeply - Pre-train, fine-tune, and align models (RLHF, DPO) - Build RAG systems, agents, and multi-agent workflows - Optimise inference and serve models efficiently - Orchestrate LLM apps with modern frameworks and APIs - Evaluate, secure, and operate LLMs in production\u003c\/p\u003e\n\u003cp style=\"hyphenate-character: '-' !important; color: rgb(0, 0, 0); font-family: 'Times New Roman'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;\"\u003eBuilt for engineers who want the full picture — from the Math of attention to the MLOps of deployment.\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e","brand":"Professional Developer Store","offers":[{"title":"Default Title","offer_id":58294912844108,"sku":null,"price":4.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0849\/7728\/7500\/files\/LLMEngineering-Cover_front_6.93x9.84.png?v=1785846990","url":"https:\/\/professionaldeveloper.store\/products\/new-product-10","provider":"Professional Developer Store","version":"1.0","type":"link"}