Large Language Models, like the ones you might interact with through APIs, are trained on massive datasets containing text and code from the internet and digitized books. This training process imbues them with an extensive understanding of language, facts, reasoning patterns, and various domains. However, this knowledge is fundamentally static, reflecting the state of information at the time the training data was collected. This inherent characteristic presents several significant limitations when building practical applications.
Every pre-trained LLM has a "knowledge cutoff date." This is the point in time after which the model was not exposed to new information during its training phase. Think of it like a snapshot of information, frozen at a particular moment. Consequently, the model is unaware of:
If you ask a model about an event that happened last week, or query it for the latest specifications of a product released yesterday, it likely won't have the answer. At best, it might state its knowledge limitations; at worst, it might attempt to extrapolate or guess based on its older data, potentially leading to incorrect or outdated responses.
Consider asking an LLM with a knowledge cutoff in early 2023 about the winner of a major election held in late 2023. It simply wouldn't know, as that information didn't exist in its training corpus.
Related to the knowledge cutoff is the inability of standard LLMs to access real-time, dynamic data feeds. They cannot:
Applications requiring truly current information cannot rely solely on the LLM's internal knowledge.
Perhaps the most common limitation encountered in enterprise or personalized application development is the LLM's inherent lack of access to private data sources. Standard models have no built-in capability to query:
Imagine building a customer support bot. While a general LLM knows how to converse politely and answer common questions, it cannot access your company's specific product manuals, troubleshooting guides, or customer history databases to provide tailored, accurate support. Feeding all potentially relevant private data into the prompt for every query is often impractical due to context window size limitations and security concerns.
When faced with queries that fall outside their knowledge base or require information they don't possess (like recent or private data), LLMs can sometimes "confabulate" or "hallucinate." This means they generate responses that sound plausible and grammatically correct but are factually incorrect or nonsensical. They might invent details, misremember facts from their training data, or combine unrelated pieces of information in misleading ways. This happens because the model's objective is often to generate coherent text sequences based on patterns learned during training, not necessarily to guarantee factual accuracy, especially concerning information it was never trained on.
The LLM's internal knowledge is confined to its training data, creating a gap between what it knows and the external environment's real-time, private, or recent information needed to answer certain queries. Red dotted lines indicate information typically inaccessible to the standard LLM.
These limitations underscore the need for mechanisms that allow LLMs to consult external information sources during the generation process. Relying solely on the model's pre-trained parameters is insufficient for tasks demanding current, specific, or proprietary knowledge. This is precisely the problem that Retrieval Augmented Generation (RAG), the focus of this chapter, aims to solve. By retrieving relevant external information first and then providing it to the LLM as context, RAG systems enable models to generate more accurate, timely, and relevant responses.
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