모두를 위한 대규모 언어 모델 LLM(Large Language Model) Part 1 - Llama 2 Fine-Tuning 해보기
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LLM(Large Language Model)의 기초 개념부터 고성능 LLM인 Llama 2 모델을 내가 원하는 데이터셋에 Fine-Tuning하는 방법까지 차근차근 학습합니다.
Intermediate
LLM, Llama, 딥러닝
Large Language Model LLM for Everyone Part 4 - Learning RAG Implementation by Building an AI Customer Service Chatbot (AICC)
This lecture covers building practical RAG (Retrieval-Augmented Generation) systems while developing an AI customer service chatbot (AICC).
What you will learn!
How to build an AI Customer Center Chatbot (AICC) using the LangChain library
Various advanced RAG techniques for building AI customer service chatbots
Various Use Cases of Retrieval-Augmented Generation (RAG) Implementation
Points to Consider When Building an AI Customer Service Chatbot
AI Customer Center Chatbot (AICC), which is expected to see a rapid increase in demand
Learn the various techniques needed for practical RAG implementation!
While creating various AI customer center chatbots (AICC),
Let's learn how to implement a high-performance RAG system!
By building a practical LLM application, the AI Customer Center Chatbot (AICC), you will learn the techniques required to implement a high-performance RAG system.
- ✅ Learn how to implement an AI Customer Center Chatbot (AICC) using the LangChain library.
- ✅ Learn various techniques for building high-performance RAG systems.
Who is this course for?
Anyone who wants to create an AI customer center chatbot (AICC)
Anyone who wants to create their own RAG system using Langchain
Anyone who wants to learn various techniques for creating a high-performance RAG system
Anyone who wants to develop a service using the latest LLM model
Player Course ✅
👋 This course requires prior knowledge of Python, Natural Language Processing (NLP), LLM, and LangChain . Be sure to take the courses below first or have equivalent knowledge before taking this course.
Q&A 💬
Q. What is AICC (AI Contact Center)?
AICC (AI Contact Center) is a system that utilizes artificial intelligence (AI) to provide customer service and support. Unlike traditional call centers, AICC uses AI technology to provide a more efficient and personalized customer experience . The following are the key features and advantages of AICC.
1. Automated response system:
Respond to customer questions in real time through chatbots and voicebots.
Provides automated responses to frequently asked questions (FAQs).
2. Natural Language Processing (NLP):
We use natural language processing technology to understand customer inquiries and provide appropriate answers.
It supports multiple languages, understands context, and can analyze emotions.
3. Data Analysis and Insights:
Analyze customer interaction data to understand customer behavior and preferences.
This allows us to provide personalized services and product recommendations.
4. 24/7 Service:
We are open 24/7 so customers can get help at any time.
Reduce waiting times and increase customer satisfaction.
The advantages of AICC are:
1. Cost savings:
You can reduce labor costs and lower operating costs.
Once built, AI systems have low maintenance costs.
2. Increased efficiency:
Reduce the burden on your agents by automating repetitive and simple tasks.
Counselors can focus on more complex and valuable tasks.
3. Personalized Services:
We provide personalized services based on your past interaction data.
We provide fast and accurate responses tailored to our customers' needs.
4. Easy to scale:
Easily scale as your business grows.
We can respond reliably even when the number of customer inquiries increases rapidly.
AICC is revolutionizing customer service and playing a vital role in helping companies deliver better customer experiences and increase operational efficiency .
Q. Is player knowledge required?
This lecture [ Large Language Model for Everyone LLM Part 4 - Learning RAG Implementation by Building an AI Customer Center Chatbot (AICC) ] covers how to build an AI Customer Center Chatbot (AICC) using the LangChain library and LLM . Therefore, the lecture proceeds under the assumption that you have basic knowledge of Python, natural language processing, LLM, and LangChain. Therefore, if you lack prior knowledge, we recommend taking the preceding lecture [ Large Language Model for Everyone LLM (Large Language Model) Part 2 - Building Your Own ChatGPT with LangChain] first.
Recommended for
these people
Who is this course right for?
ctrl95>W
Who wants to build an AI Customer Service Chatbot (AICC)
Anyone who wants to create their own ChatGPT
Deep Learning Research Job Seekers
Aspiring AI/Deep Learning researchers
Those preparing for AI graduate school
Need to know before starting?
Python experience
Prior Course Experience: [Large Language Model (LLM) for Everyone Part 2 - Building My Own ChatGPT with LangChain]
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28 lectures ∙ (6hr 45min)
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