You could say that it’s like chocolate and peanut butter – two great tastes that go really well together.

Alternatively, you might explain it in a more technical way. In essence, Retrieval Augmented Generation is when additional information is provided to the LLM as it applies its own training data and knowledge to perform a task.

According to experts at GeeksforGeeks, they describe it this way:

“In traditional LLMs, the model generates responses based only on the data it was trained on, which may not include up-to-date or specific details needed for certain tasks. RAG overcomes this by incorporating a retrieval mechanism that allows the model to access external databases or documents in real-time.”

There's also a helpful flow chart with “data chunks” and other components that illustrate how this system functions.

Imagine how this would work in practice – for instance, suppose you provide a chatbot with a set of white papers about your business and then ask it questions about your business strategy. On a personal level, if you want the AI to understand you better, you might upload personal documents such as diary entries or past writings to help it build a more accurate understanding of who you are.

Broadly speaking, RAG involves introducing any information not present in the original training set. This could be done for reasons of specificity, timing, intent, or simply to tailor the output more precisely.

Getting to the Point

I found this particularly interesting ---

At Learn By Building AI, Bill Chambers outlines a straightforward approach to RAG.

He contrasts it with the following explanation he came across at Facebook:

“Building a model that researches and contextualizes is more complex, but vital for future progress. Recently, we've made significant strides in this area with our Retrieval Augmented Generation (RAG) architecture, an end-to-end differentiable model that combines an information retrieval component (Facebook AI’s dense-passage retrieval system) with a seq2seq generator (our Bidirectional and Auto-Regressive Transformers [BART]

model). RAG can be fine-tuned on knowledge-intensive downstream tasks to achieve state-of-the-art results compared with even the largest pretrained seq2seq language models. And unlike these pretrained models, RAG’s internal knowledge can be easily modified or even expanded on the fly, allowing researchers and engineers to control what RAG knows and doesn’t know without having to retrain the entire model from scratch.” Good grief…

Chambers then presents a clear diagram showing a “corpus of documents” being connected to an LLM through user input.

That made sense: RAG means integrating specific informational resources! Of course, there are technical nuances involved, but I thought the tutorial did a solid job explaining things, so it's another resource for those wanting to grasp how RAG works in practice.

Using RAG

I also wanted to mention a tech talk by Soundararajan Srinivasan, Sr. Director of AI Program at Microsoft, and his colleague Reshmi Ghosh, a Microsoft Sr. Applied Scientist, at Imagination in Action in April, where they discussed practical applications of RAG.

Using terms like “knowledge store,” “vector database,” “orchestrator,” and “meta prompt,” Srinivasan walked through how these systems operate, highlighting their role in clarifying AI’s limitations within a given context.

The word “context” is especially important because, as he explains, a larger context window increases capability while potentially reducing memory usage.

Here are some additional reasons the presenters cited for using RAG:

• To integrate knowledge and reasoning
• To make model usage more accessible
• For efficient use of time and resources

Ghosh then explained how we determine whether a model utilizes RAG information during processing.

“You have all these different contexts that are sent along with the query to inform the model, ‘hey, here's the external knowledge you may or may not already know,’” she said.

“When designing systems with large language models, or even smaller ones like llama and phi, we're finding that by sending context in a compartmentalized format rather than fine-tuning the model, you can still get accurate factual responses.”

Ghosh also touched on multi-modality.

“You can essentially have databases containing images, voice notes, sounds, or any kind of audio, and still develop AI applications around them with similar benefits, since models are now leaning more towards utilizing RAG context and relying less on internal memory, opening new possibilities for emerging frameworks.”

This, she added, aligns well with protocols like MCP (Model Context Protocol) and A2A (Agent to Agent systems).

This is significant as we enter a new era of interfaces beyond typing—voice is now part of the mix, and image and video generation will soon offer rich alternatives to text-based models.

Some might argue we're entering a world of limitless possibilities once thought unattainable.

RAG could be one key to ensuring we maintain control and deliver the outcomes we desire. It contributes to what might be called “convergence” in a digital intelligence system. So stay tuned for further developments in these methodologies as we continue building increasingly advanced AI tools and solutions.

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