{ "item_title" : "AI Application Engineering", "item_author" : [" Cipher Orion "], "item_description" : "AI Application Engineering shifts the focus from simple LLM prototyping to building production-ready, maintainable, and dependable software systems. Bridging the gap between an impressive initial demonstration and a reliable enterprise application, this book treats generative AI as a complete software engineering discipline rather than a set of standalone prompts and APIs.Core Focus Areas & ArchitectureSystem Architecture & Model IntegrationEstablishes how models, application logic, context, storage, evaluation, and infrastructure interoperate.Focuses on building robust application interfaces, prompt/instruction pipelines, and structured generation/response validation to convert probabilistic LLM outputs into dependable data.Retrieval-Augmented Generation (RAG) & Context EngineeringRejects the notion that simply expanding context windows yields better results.Details document ingestion, knowledge grounding, vector embeddings, metadata filtering, hybrid search, reranking, and context compression to present optimal information to the model.Agentic Workflows & Multi-Agent ExecutionExtends retrieval principles to autonomous agents that plan, call external tools, and execute multi-step tasks.Covers task decomposition, tool invocation, agent coordination, and failure containment to prevent cascading errors across systems.Probabilistic Evaluation & BenchmarkingTreats testing as a rigorous engineering discipline adapted for probabilistic AI outputs.Covers dataset construction, RAG evaluation, regression testing, model comparison, and failure analysis.Reliability, Security, & OptimizationExplores failure mode mitigation, retries, timeouts, fallbacks, and graceful degradation.Establishes security boundaries around untrusted inputs/outputs and integrates human-in-the-loop controls.Optimizes performance by managing latency, operational costs, model routing, and caching.Production Governance & ObservabilityFrames compliance, data protection, and governance as fundamental engineering requirements.Covers observability, auditability, decision tracing, risk classification, and version control for prompts, models, datasets, and code.Target AudienceThis book assumes basic programming and API familiarity without requiring deep learning expertise. It is written for: Software, AI/ML, & RAG Developers scaling experimental builds into production systems.Technical Architects & Engineering Leaders designing, deploying, and governing maintainable AI infrastructure.Technical Founders & Researchers evaluating architecture trade-offs, cost controls, and real-world system reliability.From Prototype to ProductionA prototype succeeds on a curated prompt; a production application must serve thousands of diverse inputs despite changing models, imperfect data, latency spikes, and security threats. AI Application Engineering provides the definitive framework for turning LLM capabilities into scalable, operational software.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/79/819/200/9798192001127_b.jpg", "price_data" : { "retail_price" : "27.99", "online_price" : "27.99", "our_price" : "27.99", "club_price" : "27.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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