AI Generatif untuk Pemula
Kurikulum komprehensif yang menjelajahi dasar-dasar AI Generatif, Model Bahasa Besar, teknik prompt engineering, serta pengembangan aplikasi berbasis AI menggunakan alat seperti Azure OpenAI dan Power Platform.
Gambaran Umum Kursus
📚 Ringkasan Konten
Kurikulum komprehensif yang menjelajahi dasar-dasar AI Generatif, Model Bahasa Besar (LLM), teknik prompt engineering, serta pengembangan aplikasi berbasis AI menggunakan alat seperti Azure OpenAI dan Power Platform.
Kelola dasar-dasar AI Generatif dan bangun aplikasi cerdas dari awal.
Ucapan Terima Kasih: Microsoft, Azure, dan OpenAI.
🎯 Tujuan Pembelajaran
- Jelaskan bagian dalam mekanis LLM, termasuk tokenisasi, mekanisme perhatian (attention), dan output yang tidak deterministik.
- Bandingkan berbagai kategori LLM (Model Dasar, Sumber Terbuka vs. Proprietary, dan arsitektur Encoder/Decoder) untuk memilih alat yang tepat sesuai skenario bisnis.
- Evaluasi strategi peningkatan hasil model, khususnya memilih antara Engineering Prompt, Retrieval Augmented Generation (RAG), dan Fine-tuning.
- Definisikan Engineering Prompt dan jelaskan perannya sebagai antarmuka pemrograman utama bagi AI Generatif.
- Bedakan antara LLM dasar dan LLM yang telah ditunjukkan instruksi (Instruction-Tuned LLM) serta cara mereka memproses token.
- Buat prompt kompleks menggunakan instruksi, konten utama, petunjuk, dan templat.
- Bangun dan konfigurasi aplikasi generasi teks menggunakan perpustakaan
openai, mengelola variabel lingkungan, dan menyesuaikan variasi output melalui suhu (temperature). - Bedakan antara chatbot berbasis aturan dan aplikasi AI generatif yang sadar konteks sambil menerapkan Enam Prinsip Kecerdasan Buatan yang Bertanggung Jawab dari Microsoft.
- Jalankan pencarian semantik dengan mengubah teks menjadi embedding (vektor) dan menerapkan kesamaan cosinus untuk menemukan konten relevan di luar pencocokan kata kunci sederhana.
- Bangun dan konfigurasi aplikasi generasi gambar sambil menerapkan "prompt meta" untuk menentukan batas konten dan keamanan.
Pelajaran 共 7 课时 · 预计 21.0h
Pelajaran
Lesson
This lesson explores the evolution of AI from rigid, rule-based systems to modern Large Language Models that utilize tokenization, attention mechanisms, and statistical probability to generate human-like text. Students will learn how these non-deterministic models function and how to evaluate strategies like prompting, fine-tuning, and RAG to optimize AI performance for specific business needs.
This lesson introduces prompt engineering as the essential interface for guiding generative AI, distinguishing between base models and instruction-tuned models. Students learn to construct effective prompts by mastering tokenization, parameter adjustment, and structural techniques like delimiting to improve model accuracy and reliability.
This lesson explores the transition from traditional, deterministic programming to flexible Generative AI applications by mastering API security, environment configuration, and output control via temperature settings. Students will also learn to implement semantic search using embeddings and apply Microsoft’s Six Principles of Responsible AI to build reliable, context-aware solutions.
This lesson explores how to build secure, integrated AI solutions using low-code platforms like Microsoft Power Platform and advanced techniques such as meta-prompting and function calling. Students learn to bridge the gap between generative models and external data while implementing safety guardrails and deterministic configurations for enterprise-grade applications.
This lesson explores how to build trustworthy Generative AI by balancing user experience pillars, such as explainability and instructional friction, with robust security measures against threats like data poisoning and prompt injection. It also introduces the LLMOps lifecycle, covering the essential stages of ideating, building, and operationalizing AI applications to ensure reliability and performance.
This lesson explores how Retrieval-Augmented Generation (RAG) overcomes LLM knowledge cutoffs and hallucinations by grounding model responses in real-time, external data. Students will learn the technical RAG workflow—including chunking, embeddings, and vector search—while evaluating open-source models and AI agent frameworks for practical implementation.
This lesson explores the optimization hierarchy for generative AI, emphasizing that developers should prioritize prompt engineering and Retrieval-Augmented Generation (RAG) before considering resource-intensive fine-tuning. It also introduces specialized architectures like Small Language Models (SLMs) and Mixture of Experts (MoE) to help balance model performance, inference speed, and deployment efficiency.