English | Size: 5.16 GB
Genre: eLearning
Thrive in the AI era with this hands-on course that teaches you to build better AI applications using one of the most important AI techniques used in the real-world to supplement an AI model’s knowledge with proprietary or new information: Retrieval Augmented Generation (RAG).
What you’ll learn
Combine generative AI models with Retrieval Augmented Generation to build smarter AI systems
Use OpenAI APIs for text generation and processing unstructured data
Master FAISS for efficient similarity searches in massive datasets
Apply prompt engineering techniques for optimal AI responses
Build real-world AI projects like chatbots and financial analysis tools
Explore advanced RAG concepts like multimodal and agentic RAG
What Is An AI Engineer?
The short version is that an AI Engineer works on the entire lifecycle of an AI application – that is, an application that utilizes AI at its core. An AI Engineer takes AI models, including Large Language Models, and customizes them to their needs.
(If you want the long version, check out our blog post here)
That requires everything from building models using custom datasets, to training and tuning models, to deploying models and scaling them using cloud technologies.
The role is growing like wildfire, but it’s still evolving and will no doubt continue evolving as the AI landscape changes.
What Is Retrieval Augmented Generation (RAG)?
Have you ever wondered why some AI systems can feel limited, giving answers that seem a bit generic or off-target? Well that’s because they’re limited to the knowledge in their training data.
And that’s a lot of data. But it’s not everything. It doesn’t include private data, nor does it include recent data that has been created since the model was trained.
Retrieval-Augmented Generation, or RAG, addresses this by supplementing AI models with that private or new information.
Instead of relying only on what it was trained on, RAG retrieves up-to-date, relevant information from a database or document.
Here’s how it works: the system first finds the most relevant pieces of information for a given question (retrieval). Then, it uses a language model to generate a response based on that information (generation). That’s why it’s called retrieval-augmented generation!
The result is an AI that combines the best of both worlds: real-time access to external knowledge and the ability to express it clearly.
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