Building AI Agents with RAG and LangChain
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Course
Building AI Agents with RAG and LangChain
Gain job-ready skills in building AI agents in just 2 weeks. Build valuable practical experience and an industry-recognized credential. Build familiarity with RAG and LangChain.
Self-Paced
Mentored
Intermediate
Duration
Business demand for technical generative AI skills is exploding and generative AI engineers who can work with large language models (LLMs) are in high demand. This Building AI Agents with RAG and LangChain intermediate-level course builds job-ready skills that will fuel your AI career in just 2 weeks.
In this course, youll explore retrieval-augmented generation (RAG), prompt engineering, and LangChain concepts. Youll look at RAG, its applications, processes, encoders, tokenizers, and the Facebook AI Similarity Search (Faiss) library. Then, youll apply in-context learning and prompt engineering to design and refine prompts for accurate responses. Plus, youll explore LangChain tools, components, and chat models, and work with LangChain to simplify the application development process using LLMs.
Additionally, youll get valuable hands-on practice in online labs developing applications using integrated LLM, LangChain, and RAG technologies. Plus, youll complete a real-world project you can discuss in interviews.
If youre keen to boost your resume and extend your generative AI skills for applying transformer-based LLMs, ENROLL today and build job-ready skills in just 8 hours.
This course comprises two purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 8 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
- Apply the fundamentals of in-context learning and advanced methods of prompt engineering to enhance prompt design.
- Understand key concepts of LangChain, LangChain tools, components, chat models, chains, and agents.
- Apply RAG, PyTorch, Hugging Face, LLMs, and LangChain technologies for different applications.
- AI enthusiasts with basic generative AI knowledge.
- Python and PyTorch users.
- Those familiar with prompt engineering.
- Developers eager to build LLM-based agents using RAG and LangChain.
- Basic knowledge of generative AI, prompt engineering techniques, and working knowledge of machine learning with Python and PyTorch.
- To transition to a career in Generative AI fine-tuning transformers, we recommend you enroll in the full professional certificate program and work through the courses in order. Within a few months, youll have job-ready skills and practical experience on your resume that will catch an employer's eye!
Course Outline
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
The RAG framework, or Retrieval-Augmented Generation, is a method that improves large language models by combining retrieval with generation. Instead of relying only on pre-trained knowledge, a Rag AI Agent retrieves relevant information from a vector database or document store, then uses that information to generate accurate, context-aware answers. This makes Rag AI Agents highly effective for real-world use cases such as customer support and knowledge management.
LangChain AI Agents provide ready-to-use components for document loading, text splitting, embeddings, retrievers, and conversation memory. Developers can easily integrate these tools to build workflows such as summarizing documents using RAG and LangChain, creating QA bots, or scaling retrieval pipelines. With LangChain, you dont have to reinvent the wheelyou can assemble building blocks to quickly create production-ready AI solutions
Advanced prompt engineering methods include:
- Few-shot prompting for guiding the model with examples.
- Chain-of-thought prompting to encourage step-by-step reasoning.
- Self-consistency prompting to generate multiple solutions and select the best.
- Prompt chaining to break complex tasks into smaller prompts.
These methods are essential in Fundamentals of AI agents using RAG and LangChain IBM since they directly affect the accuracy and reliability of LLM-powered AI agents.
Vector databases such as FAISS, Pinecone, or Weaviate store embeddings of documents and enable fast semantic search. For Building AI Agents, this means:
- Quickly retrieving relevant chunks of text.
- Feeding precise context into prompts.
- Supporting scalable document search and personalization.
This makes them critical for deploying Rag AI Agents in enterprise settings where large datasets need to be searched in real time.Can I use LangChain to build chat models and LLM-powered agents?
Yes. With LangChain, you can build chat models with LangChain, interactive QA bots, and LLM-powered AI agents that can retrieve, reason, and act on external information. For example, in this course you will design a Gradio-based QA bot that answers questions using embedded documents and retrieval.
This course takes a hands-on approach to Applied Generative AI engineering. Youll learn:
- How to build AI Agents using LangChain, embeddings, and RAG.
- Fundamentals for improving LLM performance.
- Prompt design and refinement for better outputs.
- Practical AI labs with LangChain and vector databases.
By completing the final project, youll demonstrate your ability to create a working Rag AI Agent, ready for real-world applications.
By enrolling, you gain:
- An IBM RAG Agentic AI Professional Certificate, which strengthens your professional portfolio.
- Experience with fundamentals of AI agents using RAG and LangChain.
- A project-based learning approach, showing employers you can build real-world Generative AI applications.
- Skills that directly apply to in-demand roles such as AI developer, machine learning engineer, or prompt engineer.
Yes. This course is designed for hands-on AI projects with LangChain. Youll practice:
- Loading and splitting documents with LangChain.
- Configuring vector databases for embedding storage.
- Implementing retrieval pipelines with RAG.
- Building AI Agents such as a QA bot powered by LangChain and LLMs.
These labs ensure you graduate with job-ready skills, not just theory.
Yes. Upon successful completion, youll earn the IBM Certificate. This credential validates your expertise in building AI Agents and demonstrates your ability to apply the fundamentals of AI agents using RAG and LangChain in real-world projects. The certificate is industry-recognized and can strengthen your portfolio, making you more competitive in the job market.
Course Offering
Type of certificate
IBM Certificate
About this course
02 Modules
04 Skills
Includes
Discussion space
04 Hands-on labs
02 Practice quizzes
02 Graded quizzes
Create
Summarize Private Documents using RAG, LangChain, and LLMs
Exercises to explore
RAG with Hugging Face
RAG with PyTorch
LangChain
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