Generative AI has changed how businesses create content, use data, automate workflows, and focus on customer experiences. As businesses integrate these advanced technologies, professionals need to become more tech-savvy.
Having an understanding of large language models (LLMs), retrieval-augmented generation (RAG), and AI agents helps them build practical AI-powered solutions.
If you also want to learn generative AI, RAG, and AI agents, then investing in the right training programme is a crucial step.
5 Generative AI, RAG and AI Agents Programs
| Programme | Provider | Best suited for | Duration |
| Certificate in Agentic AI | Johns Hopkins University | Technical learners and AI practitioners | 18 weeks |
| No-Code Generative AI and Agentic AI | Johns Hopkins University | Beginners and business professionals | 12 weeks |
| Generative AI with Large Language Models | DeepLearning.AI and AWS | Aspiring AI developers | Approximately 6 weeks |
| LangChain Academy | LangChain | No-code AI applications and agentic workflows | Short course |
| Generative AI Learning Resources | Microsoft Learn | Beginners and Microsoft ecosystem users | Short |
- JHU Certificate in Agentic AI
Provider: Johns Hopkins University (JHU), Online Lifelong Learning
The JHU Certificate in Agentic AI is worth looking at. This is ideal for those who want to know how AI systems can be useful in planning tasks, using tools, and coordinating many steps. All of these will help in completing specific objectives. Unlike conventional generative AI applications that primarily respond to prompts, agentic systems can support more complex workflows.
The agentic AI certification is relevant to professionals interested in developing AI-powered applications and understanding how agentic architectures can be applied to business problems.
- Delivery and Duration: Online; 18 weeks
- Credentials: 13 CEUs from the university.
- Programme Highlights: AI agents, Agentic RAG, and multi-agent systems
Key USPs:
- Focus on agentic AI concepts and applications.
- Helps build autonomous AI agents and working AI prototypes using Python to build functioning AI-powered tools
- Relevant to learners exploring advanced AI workflows.
- Potential pathway towards technical AI implementation.
- Suitable for comparing agent development with conventional generative AI.
- JHU No-Code Generative AI and Agentic AI
Provider: Johns Hopkins University, Online Lifelong Learning
For every working individual. programming skills are not needed to start using AI. The JHU No code Generative AI and Agentic AI programme can also be looked at if you want to know how accessible tools help with AI-powered workflows. For this, one doesn’t need to have extensive coding skills.
No-code approaches can help users experiment with content generation, information processing, and task automation. They can also provide an introduction to agentic workflows before learners move towards more technical development.
- Delivery and Duration: Online; 12 weeks
- Credentials: Earn 9 CEUs from Johns Hopkins University
- Programme Highlights: GenAI, Prompt Engineering, RAG, workflow automation, and Agentic AI.
Key USPs:
- No-code approach for learners with no or very little programming experience.
- Important for business teams and people coming from non-technical background.
- The course focuses on imparting practical knowledge and the applications of generative AI.
- Ideal course for beginners to explore AI-assisted workflow automation.
- Generative AI with Large Language Models
Provider: DeepLearning.AI and Amazon Web Services (AWS)
This course makes you technically stronger. You can learn how large language models work from expertswithe years of experience in the field. It covers concepts related to LLM development, training, fine-tuning, and deployment.
For learners who want to learn generative AI and LLM fundamentals, this program can help explain the technology behind chatbots, AI assistants, and other generative applications.
- Delivery and Duration: Almost 2 weeks
- Credentials: Check Coursera for the current certificate and enrolment requirements.
- Programme Highlights: LLM fundamentals, model training, fine-tuning, evaluation, etc.
Key USPs:
- It lays the foundations of large language models.
- Covers stages in the LLM development lifecycle.
- Develops familiarity with technical considerations in AI applications.
- Useful preparation for exploring more advanced AI development topics.
- LangChain Academy
Provider: LangChain Academy
LangChain Academy is picked by developers who want to build applications powered by language models. Its learning resources are useful in broadening the overall understanding. One can know the ways of connecting models with external tools, information sources, and application workflows.
A key topic to investigate is retrieval-augmented generation (RAG). RAG allows an AI application to retrieve relevant information from a knowledge base before generating a response. This can be useful when building document assistants, enterprise search tools, and question-answering applications.
- Delivery and Duration: Online, varies by course
- Credentials: Refer to the official page
- Programme Highlights: LLM application development, RAG, information retrieval, and agent workflows, depending on the selected resource.
Benefits of doing this course:
- Practical learning resources for LLM application development.
- Relevant to RAG pipelines and information retrieval.
- Covers concepts associated with AI agent workflows.
- Useful for developers experimenting with AI application frameworks.
- Microsoft Learn: Generative AI Learning Resources
Provider: Microsoft Learn
Microsoft Learn offers structured learning modules covering generative AI, responsible AI, and AI application development. Learners can start with introductory concepts and progress towards more applied topics, depending on the learning path selected.
This option is particularly useful if you want to build foundational knowledge before specialising in RAG, AI agents, or enterprise AI applications.
- Delivery and Duration: Online
- Credentials:
- Programme Highlights: Generative AI fundamentals, responsible AI, prompt engineering, and AI application development.
Why take this course:
- Learning resources for different experience levels.
- Introductory material on generative AI and responsible AI.
- Opportunities to explore Microsoft AI services.
- Flexible learning modules for independent study.
How to Choose the Right AI Programme
Before enrolling, do check the factors listed below:
- Technical aspects: Invest in a no-code programme. If you are new to programming, then you can take a technical course, which will help you build applications.
- RAG coverage: Check document processing, embeddings, retrieval methods, and response evaluation if RAG is a priority.
- Agent development: Before enrolling, look at whether the curriculum includes tool use, planning, workflow orchestration, and agent evaluation.
- Practical projects: Pick programmes which can help you in applying concepts in the real world.
- Certification and cost: Apart from the course, check the fees, duration, and assessment requirements. Also do look at the certificate eligibility by visiting the official website.
Final Thoughts
The right programme is the one that helps you achieve your dreams. The JHU Certificate in Agentic AI is worth looking at for dynamic technical knowledge. On the other hand, the JHU no-code programme is best for professionals who want to know AI workflows.
DeepLearning.AI, LangChain Academy, and Microsoft Learn present additional ways to learn LLMs, RAG, and AI application development.
So thoroughly check all these gen AI courses by referring to the official sites, and pick the course that aligns with your area of interest.
