Grounding AI is the process of connecting large language models to real-world data to prevent hallucinations and ensure more reliable and relevant outputs.
Grounding AI tackles a common problem with Large Language Models (LLMs): AI hallucinations, where models create seemingly plausible, but factually incorrect, responses.
Grounding bridges the gap between the vast language skills of LLMs and the specifics of the real world. LLMs are trained on massive amounts of text data, but they generally lack access to industry-specific knowledge, internal company data, or even just everyday experiences and information. Grounding techniques connect the LLM's understanding of language to this type of real-world knowledge.
One popular method of grounding AI is Retrieval-Augmented Generation (RAG). This technique avoids the cost and complexity of retraining your entire LLM. Instead, RAG intercepts and enhances user prompts with relevant and timely information – helping ensure that the LLM returns a more focused and accurate response.
Today’s most advanced RAG tools are based on business entities. Entity-based RAG retrieves and augments both structured and unstructured real-time data from any source – from enterprise systems to customer 360 platforms to knowledge base docs, and more. Then, it unifies data for each business entity (customers, vendors, devices, etc.) with a data-as-a-product approach to automatically enrich LLM prompts. The end results are highly accurate and specific prompts that yield far more personalized and relevant responses.
Grounding acts as a bridge for AI, allowing an LLM to grasp the meaning behind words and connect its knowledge to real-world situations. It's crucial because LLMs are not knowledge repositories, but rather reasoning engines. While they possess a vast amount of information, this information is not constantly updated and LLMs lack access to external datasets and often have difficulty grasping context or nuance.
The main reasons for grounding AI are:
Stale knowledge
LLMs are trained on massive datasets, but these datasets have a fixed timeframe. If a model was trained in 2021, its knowledge won't reflect current events or trends. Grounding AI allows us to inject fresh information for more relevant responses.
Limited access
LLMs are trained on publicly available information. They have no access to the wealth of data stored privately in companies, like customer details or industry-specific knowledge. Grounding AI bridges this gap, allowing LLMs to leverage this valuable, context-rich information.
Hallucination prevention
Without grounding, LLMs can generate seemingly plausible but factually incorrect responses. Grounding AI helps them stay rooted in reality by providing a connection to verifiable information.
Grounding AI unlocks the full potential of LLMs. By connecting them to the real world and providing them with relevant context, you can ensure their responses are accurate and relevant, while avoiding LLM hallucination issues.
While LLMs are impressive tools, they can sometimes generate outputs that seem true but aren’t. While hallucinations can be mildly amusing, in critical situations, they can be misleading or even dangerous. Grounding AI tackles this issue head-on, offering several key benefits such as:
Imagine asking an LLM about what dinosaurs eat. It might give you the correct answer but add a long list of irrelevant details – for example, dinosaurs’ vocal capabilities or migratory habits. While technically not wrong, this "hallucination" detracts from the response's value. Grounding helps the LLM focus on the core information relevant to the prompt and avoid these unnecessary embellishments.
It should be noted that hallucinations aren't always a bad thing. In casual conversation, a little creativity can be engaging. The key is teaching the LLM when imagination is acceptable and when it’s not. Grounding AI helps developers achieve this balance, ensuring that all outputs are accurate, trustworthy, and engaging.
Many businesses rely on AI for crucial functions like customer service, often via chatbots. However, standard chatbots often come off as robotic when answering customer questions, and frequently miss the nuances contained in a particular query. A RAG chatbot bridges this gap by incorporating real-time customer data. For example, consider a customer chatting with a clothing store's chatbot. The customer asks about a specific dress she saw online but can't find in-store.
A conventional chatbot might simply say "We apologize, that item is not currently available." With RAG, the chatbot accesses the store's inventory system. It discovers the dress is actually out of stock in-store, but there are several nearby locations where it is available. The chatbot can then offer the customer the option to check nearby stores or initiate a ship-to-home order, providing a much more helpful and personalized experience.
Grounding anchors the AI's understanding in real-world context, based on real-world data. By being able to draw on both publicly available and private data sources the RG chatbot’s responses are based on factual evidence, not just its own internal models.
The real world is messy. Data can be ambiguous, nuanced, or inconsistent. AI often struggles with these complexities, leading to misunderstandings. Grounding AI bridges this gap by providing additional context.
For example, a chatbot might misinterpret sarcasm in a conversation. Consider a hair salon customer who quips, "Thanks for this great new haircut – it makes me look like a potato head." Lacking social cues, the LLM could easily answer, "Congratulations! Customer satisfaction surveys consistently rank our stylists' ability to achieve a 'potato head' look very high. Would you like recommendations for potato-themed accessories?"
Misinterpreting the sarcasm, such a response might easily compound the customer’s sense of frustration. Grounding AI can help the GenAI model understand true user sentiment by incorporating information about the sender and about previous interactions.
The takeaway from these examples is that training data doesn’t always equip a large language model with everything it needs to answer responsibly.
Grounding AI bridges these gaps, allowing the LLM to process real-world data more effectively, for more meaningful responses in a wider range of situations. Beyond preventing hallucinations, grounding empowers AI apps to be more reliable, adaptable, and ultimately, more helpful.
Pioneered by K2View, a new set of retrieval-augmented generation tools known as GenAI Data Fusion is a promising method of grounding AI models and reducing RAG hallucinations. GenAI Data Fusion accesses and augments both structured and unstructured data from private enterprise data sources in real time. It aggregates all structured data related to a single business entity (customer, employee, invoice, etc.) based on a data product approach.
Data products enable GenAI Data Fusion to access real-time data from multiple enterprise systems, not just static docs from knowledge bases. With this feature, generative AI apps can leverage RAG to integrate data from your customer 360 platform, and turn it into contextual prompts. The prompts are fed into the LLM, together with the user’s query, enabling the model to generate a more accurate and personalized response.
The K2view data product platform lets RAG access data products via API, CDC, messaging, or streaming – in any variation – to unify data from a wide variety of different source systems.
The data product-RAG combo:
Discover the best RAG tools for grounding AI – GenAI Data Fusion by K2view.