LLM

Find the paper here RAG The Retrieval-Augmented Generation (RAG) technique offers a promising approach when leveraging large language models like LLMs to build knowledge bases. Envision creating a chatbot capable of querying a collection of textbooks. A standard pre-trained LLM doesn’t inherently possess this capability. This is where RAG comes into play. RAG works by dissecting your corpus into more manageable segments or documents. Ideally, these segments should fit within the context window of the language model in use....

I’ve been working with large language models from across the spectrum, including OpenAI GPT, Anthropic, LLAMA, etc., for quite some time. For most of this journey, I was leaning towards selecting larger models (with more context windows) and cramming as much context as possible in hopes of getting better inferences and domain-specific reasoning. However, after the release of GPT-3.5-Turbo-16K, I realized the capabilities of these LLMs do not necessarily scale with the context window....

In this blog, we will discuss prompt engineering techniques used in working with large language models: zero-shot, one-shot, and few-shot learning. These methods aim to enable models to perform and learn new tasks quickly with little or no training data. Zero-Shot Learning Zero-shot learning refers to the ability of a language model to perform a task without having seen any examples from that specific task during training. This is particularly useful when there is a lack of labeled data for a given task....