Retrieval-Augmented Generation (RAG) Courses
Retrieval-augmented generation (RAG) courses can help you learn how to combine information retrieval with generative models, enhancing the quality of AI outputs. You’ll explore techniques for integrating external knowledge sources, fine-tuning models for specific tasks, and evaluating the effectiveness of generated content.
Popular RAG Courses and Certifications
DeepLearning.AI
Skills you'll gain: Retrieval-Augmented Generation, Large Language Modeling, LLM Application, Fine-tuning, Embeddings
Skills you'll gain: Retrieval-Augmented Generation, Embeddings, Vector Databases, Generative AI, Google Gemini, Gemini, Google Cloud Platform
Skills you'll gain: API Testing, Test Automation, Test Script Development, Test Tools, Application Programming Interface (API), Restful API, Software Testing, Code Reusability, Configuration Management, Java Programming, Maintainability
Skills you'll gain: Retrieval-Augmented Generation, Generative AI, Gemini, Google Gemini, Vector Databases, Embeddings, Data Pipelines, Google Cloud Platform
Google Cloud
Skills you'll gain: Generative AI, Google Gemini, Retrieval-Augmented Generation, Embeddings, Vector Databases, Large Language Modeling, LLM Application, Google Cloud Platform
Skills you'll gain: Retrieval-Augmented Generation, Prompt Patterns, Token Optimization, Memory Management, Context Management, Document Management, Embeddings, User Interface (UI), Vector Databases, Debugging
Google Cloud
Skills you'll gain: Retrieval-Augmented Generation, Embeddings, Vector Databases, Gemini, Google Gemini, Generative AI
Skills you'll gain: Retrieval-Augmented Generation, Large Language Modeling, LangChain, LLM Application, OpenAI API, Hugging Face, Generative AI, OpenAI, Web Applications, Application Development, Flask (Web Framework), Python Programming, Natural Language Processing, Web Design and Development, Web Development, Front-End Web Development, Image Analysis
Google Cloud
Skills you'll gain: Retrieval-Augmented Generation, Vector Databases, Embeddings, Google Gemini, Generative AI, Large Language Modeling, LLM Application, Responsible AI, Google Cloud Platform, Data Pipelines
DeepLearning.AI
Skills you'll gain: Retrieval-Augmented Generation, Vector Databases, Prompt Engineering, MongoDB, Token Optimization, Operational Databases, Query Languages, Metadata Management, Performance Tuning, Embeddings, Large Language Modeling, Data Pipelines
Skills you'll gain: Data Ethics, Generative AI Agents, Generative AI, Software Design Documents, Software Design, AI Integrations, Software Architecture, Agentic Workflows, Continuous Integration, Software Development, Prompt Patterns, LLM Application, Application Security, Software Engineering, Code Review, Debugging, DevSecOps
Skills you'll gain: AI Orchestration, AI Workflows, Agentic Workflows, LangChain, LangGraph, LLM Application, Tool Calling, Agentic systems, Generative AI Agents, Artificial Intelligence and Machine Learning (AI/ML), Responsible AI, Retrieval-Augmented Generation, Generative AI, Prompt Engineering, AI Integrations, Application Development, Data Visualization, Large Language Modeling, Analytics, Software Design Patterns
In summary, here are 10 of our most popular rag courses
- Advanced Retrieval for AI with Chroma: DeepLearning.AI
- Membuat Sematan, Pencarian Vektor, & RAG dengan BigQuery: Google Cloud
- Building API Test Automation Framework Using Rest Assured: Coursera
- Créer des embeddings et utiliser la recherche vectorielle et le RAG avec BigQuery: Google Cloud
- 運用 BigQuery 建立嵌入項目、向量搜尋和 RAG: Google Cloud
- Advanced Prompt Engineering and Memory Management: Packt
- エンベディング作成、ベクトル検索、BigQuery での RAG: Google Cloud
- Building Generative AI-Powered Applications with Python: IBM
- 使用 BigQuery 创建嵌入、向量搜索和 RAG: Google Cloud
- Prompt Compression and Query Optimization: DeepLearning.AI
