"17 Checklists for 100% Success in AI Projects," a strategy book on AI planning, design, and performance evaluation, has been published.

– Contains step-by-step strategies from planning to design and performance evaluation of AI products for business growth.

In an era where AI adoption is becoming a competitive advantage, countless companies are rushing to adopt generative AI and LLM, but cases leading to actual business results remain rare. Behind the flashy technology stack lies a recurring pitfall: a lack of problem definition, inadequate data preparation, and failed performance evaluations. In this context, "17 Checklists for 100% Success in AI Projects" (Book Only) begins with the fundamental question, "Why do AI projects fail?" and presents practical solutions to increase the likelihood of success.

This book is more than a simple technical manual. It views AI not as something to be "implemented," but as a "tool to solve business problems." It structures the entire process, from feasibility studies at the planning stage to architecture design, data acquisition, LLM selection strategies, workflow design, performance evaluation, and operation, into 17 checklists. It addresses every question that any company preparing an AI project has likely pondered at least once.

By explaining AI projects from a business perspective beyond technical implementation, it provides concrete hints for practical concerns faced by practitioners, such as how to structurally apply domain knowledge to generative AI, whether to build LLM in-house or utilize APIs, and how to design performance evaluations.

Author Yoo Jin-ho, drawing on his experience developing AI solutions across various industries, presents the criteria necessary to elevate AI projects from mere prototypes to commercial-grade products. His meticulous checklist reflects on-the-ground trial and error and profound reflection, and the resulting results serve as a reliable roadmap for both practitioners and executives preparing AI projects.

"17 Checklists for 100% Success in AI Projects" is a practical guide not for organizations seeking to adopt AI as a fad, but for those seeking to translate it into real results. If you want to increase the chances of success in your AI project, this book contains the checklist you should review first.

☑ Have you estimated the probability of the project failing?

☑ Is the generative AI technology the absolute ring?

☑ Have we properly understood and selected the AI technology that is right for our business?

☑ Have you properly structured the architectural elements for a serviceable AI product?

☑ How was the data obtained and sufficiently prepared?

☑ Have you properly read and processed the unstructured document?

☑ LLM: Build it yourself or use an API?

☑ SLM: Is it being introduced in a timely manner and utilized properly?

☑ Solving a Business Problem: Create an LLM Workflow or Leave it to an Agent?

☑ Have we created the appropriate prompts to create the context we want?

☑ Have you optimized RAG to improve the AI response level?

☑ Are you evaluating RAG performance?

☑ Are you evaluating the performance of the LLM model?

☑ Are you evaluating agent performance?

☑ Are you responding to AI's lies and illusions?

☑ Are you implementing a talent acquisition and management strategy?