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Product ID: OC-2024-D1DA1
Introduction to Retrieval Augmented Generation (RAG)
Learn to build RAG applications using LLMs, LangChain, and Vector Databases. Master prompt engineering, embeddings, and chatbots for real-world automation.
In partnership with
Overview
This course provides a hands-on introduction to Retrieval Augmented Generation (RAG), equipping learners with the skills needed to develop intelligent applications that integrate Large Language Models (LLMs) with vector databases. By working with frameworks like LangChain and leveraging tools such as FAISS and ChromaDB, learners will gain practical experience in building RAG-powered solutions, from extracting structured data from invoices to developing an HR policy chatbot with conversational memory. The course is designed to bridge the gap between theoretical AI concepts and real-world applications, making it ideal for Data Scientists, ML Engineers, and Software Developers looking to automate knowledge workflows.
As a stepping stone in the journey of AI-driven automation, this course sets the foundation for more advanced applications in RAG. Learners will be encouraged to explore deeper integrations, optimize model performance, and experiment with new tools in the evolving AI landscape. Whether you're looking to enhance enterprise workflows, streamline information retrieval, or build next-generation AI applications, this course provides the essential knowledge and skills to kickstart your journey into RAG development.
Skills you'll gain
OpenAI GPTGoogle Gemini LLM Framework Conversational Memory Vector Databases
What you'll learn
- Demonstrate Large Language Model capabilities in Natural Language based Automations.
- Demonstrate the use of RAG Applications in a range of problems they can solve.
- Use Vector Databases as a Storage Medium of Language Embeddings in RAG Applications.
- Develop RAG Applications using LLM Frameworks, Models and Vector Databases.
Who Should Attend
Prerequisites
This course is designed for Data Scientists, Machine Learning Engineers, AI Engineers, Data Analysts, Software Developers, and IT Engineers who want to harness the power of Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) applications. Whether you're looking to automate knowledge workflows, enhance AI-driven solutions, or integrate LLMs into real-world applications, this course provides the essential skills and tools to get started.
Basic knowledge of Python programming and an understanding of Large Language Models (LLMs) are recommended for this course. Learners should be familiar with Python syntax, working with libraries, and basic scripting, as well as have an appreciation of how LLMs process and generate natural language. Prior experience with APIs and data handling will be helpful but is not required.
Chapters
Explore a structured set of chapters designed to build your skills step by step, with practical examples and hands-on applications.
Meet your instructors
Manas Dasgupta
View my channelFrequently Asked Questions
How much do the courses at Starweaver cost?
We offer flexible payment options to make learning accessible for everyone. With our Pay-As-You-Go plan, you can pay for each course individually. Alternatively, our Subscription-Based plan provides you with unlimited access to all courses for a monthly or yearly fee.
Do you offer any certifications upon completion of a course at Starweaver?
Yes, we do offer a certification upon completion of our course to showcase your newly acquired skills and expertise.
Does Starweaver offer any free courses or trials?
Related Modules
Product ID: OC-2024-D1DA1
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4 hours of video content
Downloadable resources
Certificate of completion
Desktop and Mobile access
Manas Dasgupta