คอร์สเรียน AI เพิ่มประสิทธิภาพครบวงจร 2.0: สร้างสกิลเอเจนต์ ทำงานอัตโนมัติสำนักงาน/ข้อมูล/คอนเทนต์หลายสถานการณ์

แนะนำคอร์ส
This AI Learning 2.0 course focuses on deployable practice, rejecting empty theory. Its core is teaching students to independently use AI to handle all office tasks and truly double efficiency. It first reshapes work thinking in the AI era, distinguishing agents from ordinary chatbots, explaining large-model strengths/weaknesses, selection, and hallucination avoidance; hands-on installs QoderWork and WorkBuddy, covering image-to-PDF, merging multiple reports, turning a sales call recording into a follow-up plan. It systematically breaks down prompt-optimization logic and fully teaches Skill install/create/chaining and industry adaptation, with deployable cases: PPT generation, web scraping, knowledge-base building, paper translation, audio/video extraction, data processing; plus a checklist of newcomer pitfalls and AI-output verification. Fitting office workers, researchers, and ops; zero-foundation learners can build their own automated workflow and let AI take over repetitive work.
Course directory (condensed 24 lessons): opening — surviving the AI-era shift; deployable case showcase; the era rewards the prepared; agent vs ordinary chatbot; QoderWork download/install; QoderWork basic demo; WorkBuddy download/install; WorkBuddy basic demo; mindset shift; practice 1 — quickly merge several images into one PDF; practice 2 — one-click merge multiple reports, say goodbye to copy-paste; practice 3 — turn a customer call recording into an executable sales follow-up plan; when results are unsatisfying, what to do first; six pitfalls newcomers hit most; chapter summary; what is a large model; stop only saying ChatGPT — models fall in several classes; what makes a model strong; why one model answers like an expert and another like a hallucination; why more data to AI is not always better; why longer chats make AI drift; why AI confidently talks nonsense; how to choose among free / subscription / API / local models; what is an agent and how it differs from a chatbot.