Large Language Models (LLMs) have made many tasks easier, like making chatbots, language translation, text summarization, and many more. In the past, we used to write models for different tasks, and then there was always the issue of their performance. Now, we can do most of the tasks easily with the help of LLMs. However, LLMs do have some limitations when they are applied to real-world use cases. They lack specific or up-to-date information leading to a phenomenon called hallucination (opens new windwhere the model generates incorrect or un-predictable results.

Vector databases (opens new window)proved to be very helpful in mitigating the hallucination issue in LLMs by providing a database of domain-specific data that the models can reference. This reduces the instances of inaccurate or nonsensical responses.

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