Curso excelente para quem quer sair da teoria sobre RAG e realmente construir um assistente que consulta dados de verdade.
LLM Apps for Data: Build a RAG Analytics Assistant
Learn to build custom Retrieval-Augmented Generation (RAG) assistants that interact with your own datasets to extract powerful AI-driven insights.
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About this course
As datasets grow more complex, the ability to query and analyze information using natural language is becoming an essential skill. Adding an AI layer to your data workflows allows you to uncover insights faster and more intuitively. In this foundational written course, you will learn how to design and build a Retrieval-Augmented Generation (RAG) analytics assistant. You will move from understanding basic AI terminology to practically applying Large Language Models (LLMs) to your own datasets, transforming raw information into conversational intelligence.
What you'll learn:
- Understand fundamental LLM concepts, key terminology, and how they apply to data analysis.
- Build a foundational Retrieval-Augmented Generation (RAG) architecture to query custom datasets.
- Implement modern vector databases to efficiently store and retrieve data embeddings.
- Apply prompt engineering basics to guide your AI assistant toward accurate and relevant answers.
- Integrate modern dataframe tools to prepare and structure your data for AI consumption.
- Practice writing code snippets that connect your data pipeline to an AI layer securely.
The course begins with a clear breakdown of AI terminology and RAG fundamentals before guiding you through practical written exercises. You will read through step-by-step text explanations and code blocks to construct a reliable, AI-driven data pipeline. Designed for beginners and data enthusiasts, this course requires no prior machine learning experience. Start reading today to bring the power of AI to your analytical workflows.
What you'll get
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Short & focused
2h 30m of practical content
Certificate of completion
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LLM Apps for Data: Build a RAG Analytics Assistant
Skills demonstrated
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Behavioral pattern analysis
Foundational
1.2 hrs
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Decision-architecture frameworks
Proficient
1.4 hrs
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1.7 hrs
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LLM Apps for Data: Build a RAG Analytics Assistant
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Performance detail
Coursework summary
Lessons completed
14 / 14
Practice questions
26 / 28
Assignments submitted
4 (avg 4.5 / 5)
Capstone project
Reviewed — 4.6 / 5
Total practice
6.2 hrs
Performance benchmark
Cohort rank
Top 12% of 1,625
Time to completion
11 days (median: 22)
Mastery score
91 / 100
Practice-question score
94%
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Reviews (20)
내 데이터셋에 직접 질문하는 RAG 어시스턴트를 처음부터 끝까지 만들어봐서 정말 유익했어요.
شرح ممتاز وعملي لبناء تطبيق RAG.
Finalmente un corso che spiega RAG facendoti costruire davvero un assistente, non solo leggendo teoria su embedding e vector database. La parte in cui si collega il retriever a dati analitici reali è quella che mi ha aiutato di più a capire l'utilità pratica di tutto questo. Ho finito il corso con un progetto che uso ancora per esplorare i miei dataset.
Watching the retrieval pipeline get built piece by piece finally made RAG click for me instead of just being a buzzword.
Building the retrieval and generation pieces together instead of separately is what finally made RAG make sense to me.
ก่อนหน้านี้เคยอ่านบทความเรื่อง RAG มาหลายที่แล้วแต่ก็ยังงงๆ ว่ามันทำงานจริงยังไง พอมาเรียนคอร์สนี้ที่ให้ลงมือสร้าง assistant ตั้งแต่ขั้นตอนแบ่งเอกสารเป็น chunk ไปจนถึงทำ embedding และเชื่อมกับโมเดลภาษา ทุกอย่างก็เริ่มเข้าใจมากขึ้นทันที ชอบมากตรงที่ตัวอย่างเป็นการเชื่อมกับข้อมูลวิเคราะห์จริงๆ ไม่ใช่แค่ข้อมูลตัวอย่างทั่วไป ทำให้เห็นภาพว่าจะเอาไปใช้กับงานตัวเองยังไง จังหวะการสอนก็กำลังดี ไม่เร็วจนตามไม่ทันและไม่ช้าจนน่าเบื่อ พอเรียนจบมีผู้ช่วยที่ตอบคำถามจากข้อมูลของตัวเองได้จริงๆ ไม่ใช่แค่โค้ดตัวอย่างที่รันแล้วจบไป รู้สึกว่าคุ้มค่าเวลามากสำหรับใครที่อยากเข้าใจ RAG แบบลงมือทำจริง
Долго откладывала изучение RAG, потому что в интернете все объясняют это слишком абстрактно, а тут наконец показали на практике. Курс проводит через весь пайплайн: чанкинг документов, эмбеддинги, векторный поиск и связку с языковой моделью для генерации ответов. Особенно полезной оказалась часть про то, как подключить ассистента к реальным аналитическим данным, а не к игрушечному примеру. Единственное, чего немного не хватило — более глубокого разбора, как оценивать качество ответов ассистента на больших объёмах данных. В остальном курс дал именно то практическое понимание, которого мне не хватало после чтения статей.
This is the first course that made retrieval-augmented generation feel like something I could actually build rather than just read about. Walking through chunking, embeddings, and hooking the retriever up to a real dataset made every piece of the pipeline click. By the end I had an assistant that could answer questions grounded in my own data instead of hallucinating.
Practical RAG build, wish deployment got more coverage.
Khóa học RAG thực hành rất dễ hiểu.
Me encantó que el curso no se quede solo en la teoría de RAG, sino que te haga construir un asistente que realmente consulta datos reales. La parte de indexación de documentos y recuperación de contexto está explicada con mucha claridad, incluso para alguien que apenas empezaba con LLMs. Salí del curso con un proyecto funcional que puedo seguir ampliando.
Świetny kurs, który krok po kroku pokazuje jak zbudować asystenta RAG podłączonego do prawdziwych danych, a nie tylko przykładowego zbioru.
Solid RAG walkthrough, though the vector search part felt rushed.
Finally a RAG course that's actually hands-on.
Este curso me abrió los ojos sobre cómo funciona realmente un sistema RAG, más allá de la teoría que había leído por ahí. Construir el asistente paso a paso, desde la parte de recuperación hasta la generación de respuestas, hizo que todo tuviera sentido de una vez. Ahora entiendo por qué mis primeros intentos con embeddings fallaban tanto.
Muy buena introducción práctica a RAG, aunque me hubiera gustado ver más sobre cómo evaluar la calidad de las respuestas del asistente.
Cara membina RAG dan menyambungkan data sendiri dijelaskan dengan baik, cuma berharap bahagian vektor diperdalam lagi.
Endlich RAG praktisch statt nur Theorie.
RAG पाइपलाइन को शुरू से आखिर तक बनाना सिखाने वाला यह कोर्स सच में बहुत प्रैक्टिकल है।
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