Tag Archives: OpenAI

From Queries to Conversations: The Art of Training the Thread

Recently, literally last night, I was discussing LLMs with a couple of my friends and it became very obvious that we each had vastly different ideas / expectations of what they are.

They are not search engines, that was the one thing we all agreed. But past that we all had different ideas / definitions. And I am not even sure we all had a definition – I know now that I didn’t and I still don’t. So, this morning I did what I have been doing lately when I have a question that bothers me – I put it to a couple of my LLMs of choice Claude and chatGPT. The responses I got from them:

LLMs aren’t oracles or search engines, but rather language pattern prediction systems that can be remarkably helpful while still requiring human oversight.

ChatGPT – strong all-rounder, especially great at explanation, tone control, and multi-step reasoning.

Claude – excels at long-context thinking and “softer,” more reflective responses—Anthropic definitely trained it with a more careful tone.

Perplexity – the search-hybrid beast. Super fast, source-backed, very handy for staying close to the real-time web.

interesting, at least I thought it was. And while I am still searching for the definition I like where this is leading me.

Both claude and chatGPT actually asked what I thought of LLMs and how defined tham. And I responded to both and they both responded to my response. And it was over the interactive conversation that I thnk I got a better sense of what I was trying to ask and get to.

Even though they both suggested that I write something I intentionally did not ask them to write something for me, but chatGPT did suggest the title. Another interesting observation.

– manzoor

P.S. the title of this post was actually suggested by one of the LLMs (chatGPT) and they both suggested / implied that maybe I was going to write something.

Large Language Models

ChatGPT became publicly available in late 2022 and ever since there seems to have been a race in this AI domain. I have not really been really into the whole thing but am getting really interested.

A very high level timeline (will need to update / correct at some point)

2017 – some scientists at Google publish a paper, “Attention is all you need” proposing a new model called Transformer

2018 – GPT-1 with 117M Parameters

2019 – GPT-2 with 1.5B

2020 – GPT-3 175B

2022 – we have RLHF, Reinforcement Learning from Human Feedback, and ChatGPT

2023 – GPT-4 1T

2024 – GPT-4o

– manzoor