Did you ever ask an AI system a question only to realize its answer was outdated or missing
key company details?
That gap can be the difference between useful insights and misleading results.
In your role as a Generative AI Engineer, you'll use Large Language Models, LLMs, to
build powerful applications.
But first, you need to solve this knowledge gap.
By the end of this video, you will be able to explain the Strategic Value of Retrieval
Augmented Generation, RAG, for providing LLMs with current and private data, and understand
the learning path you're on in this module.
LLMs are incredibly capable, but they have one major limitation.
Their knowledge is static.
These models can generate language, summarize information, and provide insights, but only
based on the data they were trained on.
That means any events, products, or policies introduced after the training cutoff are unknown
to the model.
This limitation also applies to private company data.
LLMs cannot access your internal documents, emails, or databases unless you provide a
secure mechanism to do so.
Without access, the model's answers can be incomplete or even misleading.
So how do we make an LLM smarter and up-to-date without retraining it every time new data
comes in?
The answer is Retrieval Augmented Generation, or RAG.
This technique allows your model to retrieve relevant information from external sources
before producing an answer.
RAG transforms an LLM from a static knowledge source into a dynamic assistant.
Instead of relying solely on its internal memory, the model can access fresh, reliable
data from multiple sources, both public and private, before generating a response.
This capability is essential for real-world applications.
First, RAG connects the model to live public information.
Imagine a customer service bot.
Without RAG, it might give outdated product information.
With RAG, it can retrieve the latest updates, making sure customers always get accurate,
timely answers.
Second, RAG allows access to private organizational data in a secure manner.
For example, an employee could ask, what were the key takeaways from last quarter's sales
report?
The system retrieves the relevant report and feeds it into the model, which then produces
an accurate, context-aware answer.
By combining public and private retrieval, RAG ensures your AI outputs are trustworthy.
It reduces the risk of hallucination where a model invents information that isn't true.
In business, delivering grounded answers isn't optional.
It's essential for credibility and decision-making.
As a generative AI engineer, you will build RAG pipelines that connect data sources to
LLMs.
You decide which data is relevant, how to structure it, and how to optimize retrieval
so the AI system solves real business problems effectively.
In this module, you will start by creating a basic RAG pipeline in Azure.
You'll see how each component works and how data flows from source to model.
From there, we'll cover advanced optimization techniques to improve speed, relevance, and
scalability for real-world workloads.
Hands-on practice is key.
You'll index documents, connect multiple data sources, and test retrieval quality.
You'll also explore strategies for ranking, filtering, and caching retrieved content,
which ensures that your AI provides the best possible answers in seconds.
Mastering RAG is not just a technical skill.
It's a strategic advantage.
You move from relying on outdated knowledge to building AI systems that can answer current,
context-rich questions.
This increases the value of your applications and the confidence stakeholders have in your
solutions.
In short, RAG lets you build AI applications that are smarter, faster, and more reliable.
Public data ensures relevance, private data ensures context, and retrieval ensures grounding.
You'll leave this module ready to design, implement, and optimize RAG systems for business
impact.
Now, let's start building your first RAG pipeline on Azure.
You'll see how these concepts come to life and learn the skills to create AI applications
that are both powerful and trustworthy.