Learn RAG
14 expert-rated courses covering RAG. Compared by rating, price, difficulty, and job relevance so you can pick the right one.
The SkillsetCourse catalog offers a diverse range of RAG learning opportunities, including 10 courses that provide certificates. Platforms such as IBM Skills Network and Udacity feature in-depth courses, while five free options allow learners to explore RAG without financial commitment. Related skills like Python and Multimodal further enhance the learning experience and applicability of RAG in various AI contexts.
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Key Facts About RAG
- 1RAG combines retrieval techniques with generative models to improve output quality.
- 2The SkillsetCourse catalog includes 14 RAG courses across various platforms.
- 3Five free courses are available for learners interested in RAG.
- 4Ten courses offer certificates upon completion, enhancing career credentials.
- 5RAG is closely linked with skills like Generative AI and Prompt Engineering.
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Top RAG Courses
Develop Generative AI Apps in Azure
Build generative AI applications using Azure OpenAI Service. Learn prompt engineering, RAG patterns, and deployment best practices.
Generative AI
Nanodegree program focused on production-grade generative AI, including RAG, model adaptation, and multimodal applications.
5-Day Gen AI Intensive Course with Google
Self-paced five-day intensive covering LLM foundations, prompting, embeddings, agents, domain LLMs, and GenAI MLOps.
AI Engineering Buildcamp: From RAG to Agents
Project-focused buildcamp teaching RAG, tool-using agents, MCP integration, evaluation, monitoring, and capstone delivery.
Learn Generative AI in 23 Hours
Comprehensive free course covering the generative AI lifecycle, including prompting, deployment, RAG, and AI agents.
Gemini in BigQuery
This learning path provides a journey into leveraging Gemini within BigQuery for advanced data and AI workflows. Starting with foundational productivity enhancements, it progresses to building generative AI applications and culminates in mastering Retrieval Augmented Generation to mitigate AI inaccuracies. By completing this path, learners will gain practical skills in utilizing Gemini to streamline data processes, create innovative AI solutions, and ensure reliable AI outputs within the BigQuery environment.
Create Embeddings, Vector Search, and RAG with BigQuery
This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.
Building with the Claude API
Comprehensive Claude API course covering prompts, tool use, RAG, MCP, and agent workflows.
Level Up From Software Engineer to AI Engineer
Hands-on cohort for software engineers transitioning into AI through OpenAI APIs, RAG, multimodal workflows, and first-agent implementation.
Databricks Certified Generative AI Engineer Associate
Associate certification assessing design and implementation of LLM-enabled solutions, including RAG apps, deployment, governance, and monitoring on Databricks.
Complete AI Engineer Bootcamp (3 Days)
Hands-on live bootcamp focused on building and deploying a production-ready AI application, covering LLM APIs, RAG, agents, and FastAPI deployment.
Developing Generative AI Applications using Python
Project-based course building GenAI apps and chatbots with Python, RAG, and watsonx.
AI Automation Made Easy
Helps paid-media teams build no-code AI workflows that turn ad ideas into finished campaigns. RAG chatbots, n8n agents, and practical AI systems.
Smart City Engineering with Retrieval Augmented Generation
Build novel smart city platforms and unlock city insights with retrieval augmented generation (RAG) for urban intelligence applications.
Pro Tips for Learning RAG
- #1Start with foundational courses like 'Developing Generative AI Applications using Python' to grasp RAG concepts.
- #2Engage with free resources on platforms like freeCodeCamp to build initial skills without investment.
- #3Practice implementing RAG techniques in small projects to solidify your understanding and application.
- #4Follow up RAG learning with courses in related skills like Prompt Engineering to enhance your expertise.
Why Learn RAG?
- Learning RAG can significantly enhance your AI application development skills, making you valuable in tech-driven industries.
- RAG knowledge is essential for roles in AI engineering, data science, and machine learning.
- Mastering RAG can lead to innovative solutions in generative AI, boosting your career prospects.
- Understanding RAG positions you to leverage advanced AI technologies effectively, increasing your marketability.