Combining RAG with KAG to enhance LLM chatbot performance
JAN 28, 2025

JAN 28, 2025
Are you still relying on traditional chatbots for customer support? If so, you're missing out on the advanced capabilities of LLM-powered chatbots powered by RAG that can significantly improve the customer experience. Large Language Models (LLMs) have become the backbone of modern chatbots and conversational AI. They can engage in conversation as seamlessly and intelligently as a human. LLM chatbots are making waves across FinTech, eCommerce, healthcare, cybersecurity, and more, proving their versatility in various use cases.
Take OpenAI’s GPT-4, for example. It’s an exceptional AI model trained on massive datasets and generates natural-sounding human language. The impact of LLMs on conversational AI chatbots has been nothing short of extraordinary. With advancements in deep learning, LLMs can now produce accurate and contextually relevant text. However, working with Large Language Models has some hurdles.
Gaps in domain-specific knowledge, instances of generating incorrect or nonsensical information, and enhancing the LLM chatbot accuracy are some of the primary challenges of working with traditional LLM chatbots. Retrieval-augmented generation for AI chatbots effectively addresses these challenges by incorporating external knowledge sources, like databases, into the process.
LLM chatbots powered by RAG can be valuable for applications that demand specialized or constantly updated information. One of its notable advantages is that it eliminates the need to retrain LLMs for specific tasks, making it a versatile solution. Recently, RAG has gained popularity, especially in developing conversational agents.
Our blog will walk you through various aspects of RAG, the technologies that drive its core components, evaluation metrics, applications, and advancements in the field. But before that, let’s understand what is Retrieval-augmented generation (RAG).
Retrieval-augmented generation (RAG) works by taking input and retrieving a set of relevant documents from an external source. The retrieved documents are combined with the original input to provide additional context fed into a text generation model to produce the final output.
This approach is helpful in scenarios where facts are constantly changing, as it helps overcome the static nature of the knowledge stored within LLM chatbots. By integrating external information, RAG eliminates the need to retrain LLMs and allows them to generate more accurate and up-to-date responses.
Retrieval-augmented generation for AI chatbots leverages retrieved evidence to improve LLM-generated outputs' precision, reliability, and relevance. Over time, research in RAG has progressed from optimizing pre-training methods to integrating its capabilities with advanced fine-tuned models such as ChatGPT. It has enhanced RAG’s potential to generate reliable and contextually accurate results.
In the following video, IBM Senior Research Scientist Marina Danilevsky discusses the LLM/RAG framework and its benefits, such as transparency and obtaining the latest information.
The retrieval-augmented generation process can be broken down into the following four steps. These are the components of retrieval-augmented generation systems :
a) Input: This corresponds to the question posed to the system. Without a Retrieval-Augmented Generation (RAG) method, the LLM chatbot directly generates a response to the query.
b) Indexing: When RAG is implemented, a collection of related documents is first divided into smaller chunks. These chunks are converted into vector embeddings. They are stored in a vector database. At the time of inference, the system processes the query by embedding it similarly.
c) Retrieval: Relevant documents are identified by comparing the query's embedding with the stored vectors. These identified documents, referred to as "Relevant Documents," are retrieved.
d) Generation: The retrieved documents are integrated with the initial query as additional context. This combined input is sent to the model, which generates a response based on the enriched information. The resulting output is then presented to the user.
Recent advancements in Retrieval-Augmented Generation (RAG) systems have led to the development of more sophisticated approaches. Let’s explore various types of Retrieval-augmented generation approaches.
Naive RAG relies on a fundamental indexing, retrieval, and generation process. The system takes user input, retrieves relevant documents, combines them with the input, and generates a response. If the application involves ongoing conversations, the model can also use the history of prior interactions. Its retrieval process may lack precision, sometimes pulling irrelevant content, and its recall is imperfect. It can also lead to outdated or inaccurate responses, causing issues like hallucinations.
Advanced RAG focuses on enhancing the retrieval process at various stages and improving how data is indexed. The system can index data more effectively by refining the granularity of data, optimizing the index structure, adding relevant metadata, and improving alignment. The retrieval process can be optimized by fine-tuning the embedding model. Methods such as re-ranking—reshuffling the appropriate context or recalculating its relevance to the query—can improve the final output.
Modular RAG expands on the previous models by offering greater flexibility through functional modules. This system allows the integration of various components like search modules for retrieving similar documents or fine-tuned retrievers. These modules can be customized or rearranged based on specific needs. The flexibility to modify or replace modules provides significant advantages in adaptability to different problem scenarios.
A new, exciting development merges two powerful technologies: Knowledge Graphs and Retrieval-Augmented Generation systems for LLM chatbots. Combined, they create a hybrid system known as G-RAG, shaking up how we think about AI capabilities. You can improve the reliability of AI by enhancing the LLM chatbot accuracy with this combination. It reduces hallucinations in language models, where the AI might produce misleading or incorrect information.
To understand more about GraphRAG and its examples, watch this clip below, where the founder and CEO of Neo4j talks about how G-RAG can give more context to your RAG application, by combining RAG with KAG. This emerging technique is nothing but a combination of RAG and knowledge graphs.
The Knowledge Graph (KG). KGs are complex data networks whose nodes represent entities (like people, places, concepts, etc.), and the edges represent relationships. They offer a structured way of organizing and representing information and are created to be machine-readable and human-understandable.
Let's talk about triplet formation first to understand how knowledge graphs enhance RAG systems. Each piece of knowledge in a KG is captured as a triplet, which consists of three components: a head, a relation, and a tail. The head is the subject of the fact, the relation describes how the head and tail are connected, and the tail is the object to which the head is linked.
We turn to Graph Neural Networks for knowledge graphs to make these graphs even more powerful. GNNs are a special class of neural networks designed to process and learn from graph-structured data. GNNs capture relationships between directly connected nodes and the nodes that are connected indirectly. Graph Neural Networks for knowledge graphs can also propagate information across the graph layers, helping to refine the representation of each node by considering its surrounding context.
Speaking of nodes, within the structure of a Knowledge Graph, nodes represent important concepts or objects. These can be anything from people, departments, products, or locations. On the other hand, the edges are the relationships that define how these entities are connected. These could represent connections like “works in”, “located at”, or even more complex relationships, depending on the specific application of the Knowledge Graph.
Different types of KGs serve unique purposes, each contributing to the richness of data representation and its specific use cases.
a) Encyclopedic KGs: These are broad and cover general knowledge across various domains, from Wikipedia to expert databases.
b) Common-sense KGs: These are focused on everyday knowledge and the relationships between objects or events and can help enhance NLP systems.
c) Domain-Specific KGs: These are focused on niche fields, providing highly detailed and accurate information.
d) Multi-Modal KGs: These combine different media types, such as images, sounds, and videos, alongside text.
Combining RAG with KAG significantly enhances the system’s ability to gather, process, and generate relevant information. The best benefit of using Knowledge Graphs in RAG systems is that they broaden the scope of information retrieval. When the system pulls information from a Knowledge Graph, it's not just grabbing a few data points but an entire web of connected data that paints a more complete and nuanced picture.
For instance, the system can fetch a broader range of information by adjusting specific parameters in the Knowledge Graph, like the number of nodes or the depth of relationships between them.
By combining KGs with Retrieval-augmented generation for AI chatbots, we’re moving towards more accurate and reliable AI-powered chatbot solutions for businesses. This is particularly important in industries where precision and contextual understanding are critical—like healthcare, finance, or customer service. G-RAG systems in chatbots can use the structured knowledge from KGs to stay grounded in verifiable information.
From refining information retrieval and ensuring more accurate responses to offering advanced data visualization and reducing hallucinations in language models, the synergy between knowledge graphs and retrieval-augmented generation systems for LLM chatbots changes how we interact with and process information.
Combining Knowledge Graphs with RAG systems significantly enhances the accuracy and relevance of information retrieval. KGs are structured to provide direct, factual answers to queries that might otherwise be tough for standard language models to handle effectively.
For example, a KG can pull out these exact details if you want company contact details—like phone numbers. In contrast, an LLM chatbot might struggle to generate precise information independently. This is super useful for scenarios where precision matters, like fintech business inquiries, where having the correct information can make or break the outcome. Integrating RAG into software systems for customer support helps overcome challenges related to static knowledge.
A Knowledge Graph is a network of connected entities and relationships, which can be complex. But by using graph embeddings—mathematical representations that preserve these relationships—you can create sophisticated visualizations.
These visualizations can uncover patterns and insights that might not be obvious from raw data. For instance, you could plot sub-graphs or embeddings to see how different entities are interconnected. This helps you better understand the structure and relationships within the data, making it easier to analyze and draw meaningful conclusions.
LLM chatbots powered by RAG are built to predict the next word or token based on patterns they've learned, but they don’t always have a solid factual basis for their outputs. Integrating KGs into the system gives the language model a more structured, fact-based context.
Instead of predicting words based on probabilities, the model pulls relevant, semantically similar facts and relationships from the KG. This drastically reduces the likelihood of hallucination because the responses are directly grounded in the structured data within the KG. Reducing hallucinations in language models can minimize the chances of incorrect answers.
Implementing the KG-RAG approach can be broken down into three key steps: knowledge graph curation, RAG integration, and system optimization.
Curate a comprehensive and meaningful knowledge graph and collect unstructured data from various sources. Once the data is ingested, the next crucial step is identifying and extracting relevant entities. By recognizing and categorizing these entities, you can form the foundation of your knowledge graph.
The subsequent task is to establish the relationships between these entities, linking them to create a web of interconnected data. This web then becomes the basis of the knowledge graph. Finally, the graph is stored in a graph database, with embeddings generated for each entity and relationship.
Leverage the graph's vector database to retrieve relevant documents or data chunks directly applicable to a query. For instance, if a user asks a question, the system uses similarity algorithms—such as cosine or Euclidean distance—to find the most relevant information from the knowledge graph.
Selected data chunks are fed into the LLM alongside the user's query. The LLM then processes this contextual information to generate contextually aware and precise answers. These responses are accurate and enriched with specialized knowledge to help your decision-making.
This step ensures the KG-RAG framework remains effective over time. This involves fine-tuning the LLM chatbot using domain-specific data, allowing the model to generate even more precise responses as it learns from new information. Regular updates to the knowledge graph are also essential to keep the system’s data current and relevant.
Optimizing prompt engineering plays a vital role in guiding the LLM. By refining the prompts used to interact with the system, your business can ensure that the model generates the most relevant and accurate responses for a given query. This is one of the most critical implementation steps of the KG-RAG approach.
Soon, a significant area of ongoing research will be optimizing hybrid approaches, where RAG systems will be combined with fine-tuned models to achieve better results. There is considerable interest in expanding the roles and capabilities of LLMs to enhance RAG systems further. However, the application of scaling laws to RAG systems is still poorly understood, which remains a key area for further investigation.
For RAG systems to be viable in real-world applications, they must be engineered to meet the rigorous demands of production environments, balancing performance, efficiency, security, and privacy. While most research has focused on text-based tasks, there is growing interest in making RAG systems multimodal.
Finally, as G-RAG systems in chatbots are integrated into more complex applications, there is an increasing need for more refined evaluation metrics and tools. These tools should be capable of assessing various factors, including contextual relevance, creativity, factual accuracy, and content diversity. Improving the interpretability of RAG systems is a critical area for future research.
RAG systems have undergone significant progress, with the emergence of more sophisticated frameworks that offer greater customization, boosting both performance and versatility across various fields. The growing need for RAG applications has driven the rapid development of AI technologies to enhance the multiple components of these systems.
As a leading data & AI development company, Webelight Solutions Pvt. Ltd. specializes in creating AI-powered chatbot solutions for businesses. Our team can help improve your business decision-making with futuristic AI/ML solutions. Our AI chatbots use advanced emotion detection to power strategies and personalize interactions, enhancing customer service by recognizing and responding to user emotions.
Retrieval-augmented generation (RAG) is a technique that enhances large language models (LLMs) by retrieving relevant documents from an external source, combining them with the original input, and feeding this enriched context into the model for generating more accurate responses.