Beyond LLMs: How Multimodal RAG is Changing the Game

Large Language Models (LLMs) have taken the AI world by storm, excelling in tasks that require natural language understanding and generation. Trained on vast amounts of data, they can produce coherent, context-aware responses —though they are far from perfect.

While highly capable, LLMs have notable limitations. Their knowledge is confined to the data they were trained on, making them prone to outdated or incomplete information. Additionally, they can generate responses with high confidence, even when incorrect, and their decision-making processes often lack transparency (Figure 1).

Figure 1: LLM Limitations

Enter RAG: A Smarter Approach

Retrieval-Augmented Generation (RAG) enhances LLMs by providing them access to real-time, external knowledge. Rather than relying on inference, RAG retrieves relevant documents, databases, or structured content before generating a response—grounding AI in facts rather than speculation. Figure 2 illustrates how a simple RAG solution works.

Figure 2: How RAG Works

But what if we could go even further?

Multimodal RAG: Unlocking the Full Potential of AI

We live in a multimodal world, where knowledge is not just text—it’s images, videos, audio, and more. Traditional RAG systems focus solely on text retrieval, overlooking the full spectrum of available information. Multimodal RAG changes that. By integrating text, images, video, and audio, it delivers a richer, more context-aware AI experience (Figure 3).

Figure 3: Multimodal RAG: A Game Changer

Organisations generate vast amounts of multimodal data—text, images, videos, and audio—but unlocking its full value requires advanced analysis and retrieval solutions. Multimodal RAG, addresses this by combining multimodal search with generative AI, ensuring more relevant, context-aware responses.

Multimodal RAGs Patterns

Different design patterns exist for building multimodal RAG systems. Depending on the specific requirements and application, one or a combination of these patterns can be adopted to create an optimal solution. The two primary patterns are:

Media as Text:

This approach is ideal when direct access to raw media is unnecessary (e.g., user query is text-only format). Instead, metadata is extracted from the media and converted into text embeddings. These embeddings are stored in a vector database, enabling semantic search. When a user submits a query, it is transformed into the same embedding space to retrieve the most relevant information. The retrieved context is then leveraged by the LLM to generate accurate responses (Figure 4).

Figure 4: Multimodal RAG: Media as Text

Media as Metadata:

This approach is ideal when direct access to raw media is required, such as for multimodal user queries. The media content is transformed into a multimodal vector representation, enabling efficient similarity-based retrieval. When a user submits a query, it is also converted into the same multimodal vector space. A vector database is then used to retrieve the most relevant information, which serves as context for the LLM to generate an informed response (Figure 5).

Figure 5: Multimodal RAG: Media as Metadata

Applications and Use Cases of Multimodal RAG

Multimodal RAG integrates diverse data types to enhance AI responses, driving innovation across industries such as customer service, content creation, healthcare, and e-commerce (Figure 6).

Figure 6:  Multimodal RAG: Applications and Use Cases