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Category: "AI Game Production"

Books

  1. Rust for LLM Inference
    Building High-Performance LLM Inference Engine from Scratch
    Steve Publications

    Learn how modern LLM inference engines work by building one from scratch in Rust. From transformers and tokenization to KV caching, quantization, batching, and GPU optimization, this book combines theory, hands-on code, and performance engineering to help you create fast, production-ready AI systems.

  2. LLM Quantization Recipes
    A Practical Guide to Compressing Large Language Models Without Losing Their Intelligence
    Steve Publications

    LLMs are too big for single GPUs, but quantization fixes that. This book cuts through the hype to show you how to actually compress models using GPTQ, AWQ, GGUF, and NF4 without losing quality. You get real benchmarks, working code, and a clear way to pick the right tool for your hardware. Stop guessing and start deploying efficient models today.

  3. Outlast the Tools
    A Game Producer's Field Guide to AI
    Rob Sandberg

    Game producers already have most of what AI collaboration requires. They have been managing context, decomposing work, and evaluating output for years. This book is the map from where they are to where the work is going.

  4. No Description Available
  5. Systems
    How AI Scales
    Nageswar Keetha

    Systems: How AI Scales reveals how production AI handles thousands of requests, shared models, queues, state, failures, security, evaluation, cost, and continuous change. Through intuitive stories, practical architectures, and accessible mathematics, discover what it takes to transform an AI capability into a service people can trust.Volume V of The AI Systems Series.

  6. Agent
    How AI Takes Action
    Nageswar Keetha

    A model can recommend an action. But what gives it permission to act—and how does it verify what happened? Agent explores tools, authority, planning, execution, recovery, security, and oversight. It begins where Reasoning ends and leads to Systems, where one successful action must become a dependable service.

  7. Reasoning
    How AI Solves Problems
    Nageswar Keetha

    A fluent answer is not necessarily a reasoned answer. Through stories, examples, visual models, and real mathematics, Reasoning reveals how AI represents problems, searches possibilities, and verifies conclusions. It begins where Context ends—and stops at one crucial boundary: a decision is not an action. That is where Agent begins.

  8. Context
    Drive the Known to Derive the Unknown
    Nageswar Keetha

    The known surrounds us, but only some of it becomes context. The unknown is what we ask AI to resolve. How does a fluent model know what matters—to this user, from this source, at this moment? Context follows one ambiguous instruction through retrieval, RAG, memory, graphs, MCP, provenance, security, and evaluation—showing how AI drives the known to derive the unknown.

  9. Building AI That Knows Your Data
    RAG From Scratch · Volume I · Foundations
    JC Marin

    Your company's answers are already written down. This book builds the machine that finds them.

  10. Beyond Code Generation
    AI Across the Modern SDLC
    pankaj bisht

    AI in software development is about much more than generating code.Beyond Code Generation explores how AI can support the entire software development lifecycle—from understanding requirements and designing systems to development, testing, code review, deployment, and operations.

  11. Vectored Conversational AI Testing
    Structuring Free-Flowing Dialogue Into Measurable AI Behaviour
    William Argo

    “If real users rarely stop at a single prompt, then testing shouldn’t either.” 

  12. The Hyper-Drive Algorithms ​From Raw Python Formulas to High-Performance GPU & Machine Code
    From Raw Python Formulas to High-Performance GPU & Machine Code
    AhmedAdawy

    Make your Python AI code up to 100× faster using NumPy vectorization, Numba, parallel execution, and GPU acceleration. Learn through practical benchmarks and real-world optimization examples.

  13. No Description Available
  14. The Prompt Injection Bible
    Attacks, Defenses, and the Architecture of Trust in LLM Systems
    Ismail Tasdelen

    Prompt injection is the SQL injection of the AI era — and most LLM apps are wide open. This hands-on field manual shows application security engineers how to attack and defend LLM systems: direct, indirect, multimodal, and agentic injection, with working code, labs, and ASR benchmarks you can run in CI. Break it, then build the defense-in-depth that holds in production.

  15. THE ARCHITECTURE OF THOUGHT Applied Mathematics in Large Language Models & GenAI
    Applied mathematics in large language Models &GenAI
    AhmedAdawy

    Move beyond the API. Dismantle the AI black box and build generative engines from scratch with pure Python and NumPy. Master the profound geometric principles and applied mathematics driving LLMs and Transformers. Transform from a mere consumer into an elite AI innovator by writing the core mathematical architecture yourself—no shortcuts, no frameworks, just pure engineering excellence.