Cobus Greyling (2)

Large Language Models Excel At In-Context Learning (ICL)

Cobus Greyling

Studies have shown that, when supplied with a contextual reference at Inference, LLMs opt to make use of the contextual...

Retrieval Augmented Fine-Tuning (RAFT)

Cobus Greyling

Adapting Language Model to Domain Specific RAG...

Using RAFT, when presented with a question and a batch of retrieved...

DRAGIN: Dynamic RAG Based On Real-Time Information Needs Of LLMs

Cobus Greyling

A study introduced a novel approach to RAG but more importantly the study highlighted a number of shortcomings of...

A Study Comparing RAG & Fine-Tuning For Knowledge Base Use-Cases

Cobus Greyling

The selection of technology should be driven primarily by the requirements and goals of a particular use-case or...

RAT — Retrieval Augmented Thoughts

Cobus Greyling

Let me first start with a few general observations…

There is a tension between achieving efficiency within...

A Short History Of RAG

Cobus Greyling

One of the most popular themes currently around Large Language Models is the idea of Retrieval Augmented Generation...

Chain-of-Instructions (CoI) Fine-Tuning

Cobus Greyling

This approach draws inspiration from Chain-of-Thought (CoT) prompting which generates step-by-step rationales from...

Performing Multiple LLM Calls & Voting On The Best Result Are Subject To Scaling Laws

Cobus Greyling

More LLM calls enhance performance on easy queries but diminish it on hard ones.

So what scaling laws can be...

Exploring the Purpose, Power & Potential of Small Language Models (SLMs)

Cobus Greyling

A number of very capable Small Language Models (SLMs) have been open-sourced recently. In this article you will find...

Self-Reflective Retrieval-Augmented Generation (SELF-RAG)

Cobus Greyling

The SELF-RAG framework trains a single arbitrary language model to adaptively retrieve passages on-demand.To generate...

Kore.ai Technical Blog

Large Language Models Excel At In-Context Learning (ICL)

Studies have shown that, when supplied with a contextual reference at Inference, LLMs opt to make use of the contextual...

Retrieval Augmented Fine-Tuning (RAFT)

DRAGIN: Dynamic RAG Based On Real-Time Information Needs Of LLMs

A study introduced a novel approach to RAG but more importantly the study highlighted a number of shortcomings of...

A Study Comparing RAG & Fine-Tuning For Knowledge Base Use-Cases

RAT — Retrieval Augmented Thoughts

A Short History Of RAG

Chain-of-Instructions (CoI) Fine-Tuning

This approach draws inspiration from Chain-of-Thought (CoT) prompting which generates step-by-step rationales from...

Performing Multiple LLM Calls & Voting On The Best Result Are Subject To Scaling Laws

More LLM calls enhance performance on easy queries but diminish it on hard ones.

So what scaling laws can be...

Exploring the Purpose, Power & Potential of Small Language Models (SLMs)

A number of very capable Small Language Models (SLMs) have been open-sourced recently. In this article you will find...

Self-Reflective Retrieval-Augmented Generation (SELF-RAG)

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