Inferencing

Inferencing in large language models (LLMs) is the process of generating responses based on input prompts. It's powered by the attention mechanism, which allows the model to focus on relevant parts of the input and its vast learned knowledge.

When you provide context alongside a prompt, the model's attention layers weigh this new information against its pre-trained data. This context-enriched understanding guides the response generation, often leading to more accurate or tailored outputs.

Retrieval-Augmented Generation (RAG) takes this a step further. Instead of relying solely on provided context or the model's internal knowledge, RAG dynamically fetches relevant information from an external database. This approach acts as a real-time fact-checker, reducing the risk of AI "hallucinations" - plausible but incorrect responses.

The attention mechanism is crucial in both standard inferencing and RAG. It determines which parts of the input (including retrieved information in RAG) are most important for answering the query. Think of it as the model's way of deciding what to focus on, much like how humans prioritize certain details when problem-solving.

By leveraging these techniques, LLMs can provide more accurate, contextually appropriate responses across a wide range of applications.

See you tomorrow!