RAG and AI Applications

Building retrieval-augmented generation, custom GPT and grounded AI products.

Articles

How to Build Training Datasets and Fine-tune ChatGPT
2023-11-29
Large-language models (LLMs) are great generalists, but modifications are required for optimisation or specialist tasks. The easiest choice is Retrieval Augmented Generation (RAG) with in-context, which has become easier with OpenAI's custom GPTs. The hardest and most unattainable option is creating a specialist LLM from scratch. But you can achieve much of it by simply adjusting an existing LLM like ChatGPT, a process called fine-tuning. You can do that with open-source or commercial models...
OpenAI Custom GPTs: A Content Backdoor to Omniscience?
2023-11-09
Today, I received access to the new custom GPT feature on ChatGPT, and it appears to do what Sam Altman demonstrated. The implications are far-reaching, beyond the death of the RAG business model. How can OpenAI achieve and capitalise on Omniscience in a world of organisational data silos and increasingly defensive content creators and legislators?
Is OpenAI killing Retrieval Augmented Generation?
2023-11-07
OpenAI's DevDay announcement yesterday addresses issues I wrote about in the infeasibility of RAG after building Llamar.ai this summer. Did I get it wrong? Working through the details will take some time, but interesting immediate observations are apparent.
Llamar.ai: A deep dive into the (in)feasibility of RAG with LLMs
2023-09-27
Over four months, I created a working retrieval-augmented generation (RAG) product prototype for a sizeable potential customer using a Large-Language Model (LLM). It became a ChatGPT-like expert agent with deep, up-to-date domain knowledge and conversational skills. But I am shutting it down instead of rolling it out. Here is why, how I got there and what it means for the future.