Author(s): Nicholas Poon

Originally published on Towards AI.

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You’ve probably heard about Large Language Models by now, as they’ve been making significant strides in the AI landscape recently. Therefore, understanding the salient methods researchers use to bolster their LLM model is indispensable for any beginner or enthusiast. If you have no idea, no worries — I’ve got you!

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Retrieval-Augmented GenerationPromptingChain of ThoughtFew-Shot LearningReinforcement Learning from Human Feedback

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This is one of the methods that is super common and really effective. RAG is a technique that uses external knowledge to provide a response and can be broken into two parts:

Retrieval: Searches a knowledge base for relevant documents based on the input query.

Generation: Uses a generative model (normally an LLM) to produce responses by synthesizing the retrieved information with the query.

Purpose?

LLMs have limited knowledge, much like a student who has only read a few books on a topic (some may be outdated). To help this student answer questions more accurately, we can provide them with additional reference materials that are current and relevant. Most importantly, this approach helps mitigate Hallucination, where the model generates seemingly… Read the full blog for free on Medium.

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Published via Towards AI