Transformers for Natural Language Processing and Computer Vision: Specify, verify, and defend production LLM, agentic, RAG, MCP, and multimodal AI systems

by Denis Rothman

Build transformer systems that reason, retrieve, use tools, and understand images, then prove them in production with semantic blueprints, graded pass/fail evaluation, RAG, MCP agents, fine-tuning, and self-hosted inference.

Key FeaturesPaste-ready semantic blueprints your copilot turns into working code in your own stackFormal Verification & Validation (V&V) harnesses that grade builds on pass/fail criteriaEngineer RAG, MCP agents, memory, adaptation, and multimodal AI across frontier and open-weight modelsBook DescriptionTransformer code is easy to generate, but production judgment is not. Teams must choose models, assemble context, control tools, measure quality, protect data, and manage cost. Transformers for NLP and Computer Vision, Fourth Edition, provides a framework across frontier APIs and open-weight models.

This edition bridges the Third Edition’s transformer and computer vision foundations with the Fourth Edition’s focus on reasoning models, RAG, agents, multimodal systems, and self-hosted inference. Rather than competing with copilots at producing short-lived code, it publishes the layer copilots cannot eighteen Semantic Blueprints you can paste into your copilot, each defining interfaces, contracts, prohibitions, and a harness that grades the result against pass/fail criteria. You will cover transformer architecture, KV caches, reasoning models, context engineering, API gateways, production RAG, MCP agents, memory, LoRA/QLoRA, guardrails, red teaming, LLM-as-a-judge evaluation, observability, vision-language models, document understanding, vLLM serving, quantization, and sovereign deployment.

Every chapter follows a blueprint-first method, links implementation choices to cost, security, governance, and auditability, and provides hands-on work for Colab or consumer GPUs, with enterprise notes for scaling acceptance tests.

What you will learnRead the 2024-2026 shift and what it changed about the jobInstrument attention, KV cache, MoE, and reasoning models on free GPUsEngineer budgeted, verified context beyond prompt engineeringBuild provider-proof gateways, RAG retrieval contracts, and MCP agentsDecide between fine-tuning and context with defensible matricesAssemble an evaluation spine with golden sets and calibrated judgesRoute multimodal pipelines and serve them with vLLM-class enginesCompose, evaluate, and certify a complete production AI systemWho this book is forThis book is for solution architects, senior AI/ML engineers, platform engineers, software developers, data scientists, and applied researchers who must move LLM, agentic, and multimodal systems from prototype to production under security, cost, and audit constraints. It also serves readers of the first three editions who need the bridge from making a model run to proving a system ships, the courses built on those editions, and CTOs who buy architectural judgment rather than tutorials.

Intermediate to working Python is assumed, and every exercise runs on free-tier hardware.

Table of ContentsWhat Are Post-Discovery Era Transformers?The Architecture — Attention, Tokenizers, Embeddings, and the KV CacheReasoning Models and Inference-Time ComputeFrom Prompt to Context EngineeringThe API Layer as ContractRAG — The Retrieval ContractOrchestration Invariants&

Kindle Edition

Expected publication February 9, 2027