In recent months, Large Language Models (LLMs) have profoundly modified the best way we work and interact with technology, and have proven to be helpful tools in various domains, serving as writing assistants, code generators, and even creative collaborators. Their ability to know context, generate human-like text, and perform a wide selection of language-related tasks has propelled them to the forefront of artificial intelligence research.
While LLMs excel at generating generic text, they often struggle when confronted with highly specialized domains that demand precise knowledge and nuanced understanding. When used for domain-specific tasks, these models can exhibit limitations or, in some cases, even produce erroneous or hallucinatory responses. This highlights the necessity for incorporating domain knowledge into LLMs, enabling them to higher navigate complex, industry-specific jargon, exhibit a more nuanced understanding of context, and limit the danger of manufacturing false information.
In this text, we’ll explore considered one of several strategies and techniques to infuse domain knowledge into LLMs, allowing them to perform at their best inside specific skilled contexts by adding chunks of documentation into an LLM as context when injecting the query.
Here’s a breakdown of how it really works :
