Training vs. Grounding in LLMs: Understanding Two Fundamental Approaches to AI Knowledge

In the rapidly evolving landscape of large language models (LLMs), two critical concepts often create confusion: training and grounding. While both approaches aim to enhance AI capabilities and accuracy, they represent fundamentally different methodologies for how language models acquire, access, and utilize knowledge. Understanding these differences is crucial for making informed decisions about AI implementation strategies.

What is Training in LLMs?

Training is the foundational process through which language models acquire their core knowledge and capabilities. During training, models learn patterns, relationships, and information from vast datasets containing text from knowledge bases, articles, websites, product manuals and other sources. This process occurs before the model is deployed and involves complex mathematical optimization where the model adjusts billions of parameters to predict and generate human-like text. The knowledge obtained from such an exercise belongs to a fixed point in time. You can of course retrain a model to update it but that represents a substantial computational investment.

“It serves a bit as a reality check where the LLM gets told how to address a specific situation rather than guessing how to do it”

What is Grounding in LLMs?

Grounding represents a dynamic approach where language models access external information sources to enhance their responses. It serves a bit as a reality check where the LLM gets told how to address a specific situation rather than guessing how to do it. Instead of relying solely on internally stored knowledge, grounded models query databases, knowledge bases, APIs, or document repositories to obtain current, specific, and authoritative information relevant to user queries. In the case of an Agentic architecture, they can invoke Agents to accomplish specific tasks and one of the current most common ways to do this is to use MCP (Model Context Protocol). Grounded knowledge is dynamic and can be updated in real-time as external sources change. It means that the language model now has access to the most current information, domain-specific data, and proprietary content that wasn’t included in the original training dataset. Grounding also allows for personalization, as models can access user-specific information or context-relevant data sources.

Key Differences and Trade-offs

Timeliness and Currency

The most significant difference between training and grounding comes down to how current the information you need is. Trained models operate with a knowledge cutoff date, beyond which, they cannot provide information about events, changes, or developments. This limitation is particularly problematic in fast-moving fields like technology, finance, or current events.

“Grounding addresses this limitation by accessing real-time information sources.”

Grounding addresses this limitation by accessing real-time information sources. A grounded model can provide current prices, level of a stock of a device, status of network equipment, balance of an account, network speed, …, or the latest product specifications because it queries live data sources rather than relying on historical training data.

Accuracy and Reliability

Training provides broad, general knowledge but may include inaccuracies/hallucinations or outdated information that cannot be corrected without retraining. The model might confidently present incorrect information just because it was part of its training set.

“The accuracy of grounded responses depends on the quality of external sources, the effectiveness of retrieval mechanisms, and the model’s ability to properly synthesize retrieved information.”

Grounding can improve accuracy by accessing authoritative, verified sources, but it introduces new potential points of failure. The accuracy of grounded responses depends on the quality of external sources, the effectiveness of retrieval mechanisms, and the model’s ability to properly synthesize retrieved information. This is why you might want to carefully consider the source of your grounding material and the way it is all orchestrated.

Specificity and Personalization

Trained models excel at providing general knowledge and reasoning capabilities but struggle with specific, personalized, complex or proprietary information that wasn’t included in training data. They cannot access user-specific data or adapt to individual contexts without additional fine-tuning.

Grounding enables high levels of specificity and personalization by accessing relevant data sources tailored to individual users or specific domains. Enterprise applications particularly benefit from this capability, as grounded models can access internal databases, user profiles, and context-specific information.

Hybrid Approaches: The Best of Both Worlds

Modern AI systems increasingly employ hybrid approaches that combine training and grounding to maximize benefits while minimizing limitations. These systems leverage trained knowledge for general reasoning and language capabilities while using grounding to access current, specific, and authoritative information.

Complementary Strengths

Training provides the foundation for language understanding, reasoning capabilities, and broad knowledge that enables effective communication and problem-solving. Grounding supplements this foundation with current, accurate, and specific information that enhances response quality and relevance.

Implementation Strategies

Successful hybrid implementations require careful orchestration of when to rely on trained knowledge versus when to query external sources. Systems might use trained knowledge for general concepts and reasoning while grounding for specific facts, methodology, and user-specific information.

Looking forward 

The future of LLM development likely lies in increasingly sophisticated hybrid approaches that seamlessly blend trained and grounded knowledge. Understanding the fundamental differences between training and grounding highlights the importance of not solely relying on a language model but thinking about the ecosystem that surrounds it and orchestrates the various elements at play.