Cobus Greyling

The Shifting Vocabulary of AI (Updated)

The vocabulary in Generative AI and Conversational AI is evolving rapidly, introducing new terms that shape how we...

Can Minor Document Typos Comprehensively Disrupt RAG Retriever & Reader Components?

Retrieval-Augmented Generation (RAG) is effective in leveraging LLM in-context learning (ICL) capabilities.

But are we...

Data Design For Fine-Tuning To Improve Small Language Model Behaviour

Teaching Small Language Models to Self-Correct & Reason by using creative data formats for fine-tuning data. Via Prompt...

Improve Conversational UIs Using Social Intelligence

For Conversational User Interfaces to improve, the reliance on social intelligence will need to be leveraged. Many now...

Agentic Search-Augmented Factuality Evaluator (SAFE) For LLMs

This study demonstrates that an LLM agent can outperform crowdsourced human annotators…

FaaF: Facts As A Function For Evaluating RAG

There has been instances where another Language Model is used to vet the RAG output, which fails to detect incorrect...

Disambiguation: Dynamic Context For Effective RAG Question Suggestions

This approach reminds of a technique initially implemented by IBM Watson called disambiguation. Where ambiguous input...

FIT-RAG

FIT-RAG: Are RAG Architectures Settling On A Standardised Approach?

As RAG is being used, vulnerabilities are emerging...

Challenges In Adopting Retrieval-Augmented Generation Solutions

I have thoroughly examined some of the recent academic papers on RAG (Retrieval-Augmented Generation) and have...

Adaptive-RAG

It is evident that there needs to be a balance between query time, quality in terms of performance, but also efficiency.