This Blog Post Continues from where the Part-1 Stopped.
GPT-3 to ChatGPT: Making AI Conversational
Following the success of GPT-2, OpenAI released GPT-3 in 2020. It was massively larger, having a size of 175 billion parameters (compared to just 1.5 billion Parameters of the largest GPT-2 Model). Because of its huge size, GPT-3 was very powerful and could perform tasks with very few examples (a concept that is called "few shot learning"). However, it had usability problems. Because it was still just a "next word predictor", it would often ramble and generate nonsensical content instead of answering a direct question. For example, if we prompted GPT-3 like:
Can you tell me why the sky is blue ?
It's response could be:
The young boy looked up at his father, waiting for an answer. The father sighed, leaning against the old oak tree, and began to explain the physics of Rayleigh scattering in a way a child could understand.
(That's a real response from GPT-3 btw)
This sounds like a part of a story rather than an answer to the question we just asked it.
To fix this, OpenAI developed InstructGPT. They used a technique called Reinforcement Learning from Human Feedback (RLHF). Essentially, they made humans rank the model's responses to teach it what sort of answers we expect for a given question. This changed the model from merely predicting text to actually following user instructions.
This alignment work paved the way for GPT-3.5. On 30th November, 2022, OpenAI took this instruction following model and wrapped it in a simple chat interface. Thus was born ChatGPT as we know it today !!
The underlying technology wasn't entirely new to the AI Research community but the user experience was revolutionary. Suddenly, anyone could interact with a computer program in natural language and chat with it, like we would with any other person. ChatGPT reached 100 million users in just two months and also completely changed the public perception of AI and kicked off the current generative AI boom.
Democratizing AI: The Rise of Open-Weight Models
From GPT-3, all the frontier models by OpenAI have been closed weights. Other companies like Google (with Gemini Models) and startups like Anthropic (with Claude Models) started working on LLMs too, but they were all closed source.
As early as 2022, projects like BLOOM proved that large scale, open access models were possible. But the true explosion of Open Weight LLMs began with Meta announcing LLaMA. Initially, they only shared the model weights with approved researchers, but the weights quickly leaked online. This was a text completion model, similar to GPT-3, but smaller in size (The Largest was 65B) but was pretty close in capabilities. Soon after the leak, researchers at Stanford released Alpaca, a fine tuned version of LLaMA that was trained to follow instructions for a fraction of the cost. Then came the 7B Mistral model released by an European Startup Mistral, which made people very excited because it was a very capable model at a much smaller size (7B). And Mistral was the first model that I ran on my own computer and it was so much fun.
More and more startups as well as large companies started releasing Open Weight Models, which could be run by anybody, provided they have the Computing Resources to do so (Like a large enough GPU to fit it). These models allowed smaller companies and startups to have full control over the infrastructure to host the models, ensuring that data never leaves their systems. It also allowed individuals to use it in their projects or just experiment for fun. Platforms like Hugging Face became a central hub for this movement. Anyone could upload new models as well as fine tuned versions of these models which could be used by other people.And recently we have tools like Ollama, that has greatly simplified the efforts needed to run an LLM on our computers. It's just a matter to running a simple command and boom, you have a local LLM that you can talk to, running on your very own hardware, which would work even if you don't have internet.
The Modern Era: GPT-5, Gemini, Claude and Beyond
The modern generative AI landscape has become an arms race. Companies and Investors are pouring in Billions of Dollars of Capital into development of these models and they have become very capable and quite useful.
OpenAI continued to push the frontier with GPT-4 (which is rumoured to be a Trillion Parameter Model !!!), which introduced significantly better reasoning and the ability to process images alongside text. They later expanded this with their "O-series" (O1 and O3) reasoning models, which were trained to systematically "think" through complex math, coding and logic problems step by step before answering. I still remember the shock and amusement as I read their blogpost at midnight while travelling in a bus. It was crazy to see a language model solve super complex math problems which I don't even understand and solve stuff that would take even the best mathematicians several hours in several minutes. And now we have GPT-5 family of models (GPT 5.4 being the latest) that power today's ChatGPT. They had managed to unify the thinking capabilities of O1 and O3 with the quick text generation capabilities of GPT-4. GPT 5 can hence think if the problem is huge and complex or provide straightforward quick responses for simpler queries.
Simultaneously, Google introduced the Gemini family of models. Unlike earlier models that had vision or audio capabilities added on later, Gemini was built from the ground up to be natively multimodal, meaning it could understand and process text, code, images, audio and heck even video ! It also has a massive context window of 1 Million Tokens (or the newer ones 2M), which means it can process several books at the same time or a whole movie !!! This is the model that is currently assisting me with the research work for this blog. It's the same model that helps me with most of my assignments and I wouldn't have survived the college math courses without Gemini,
Anthropic, a company founded by former OpenAI researchers, released the Claude series. They aimed to build Safety First AI by following Constitutional AI principles. Nowadays, Claude is one of the best AI Models for Software Development. People who use Claude Code have told me it's insane and I have seen it work wonders during our hackathons. Interestingly, Claude Code even drove NASA's Perseverance Rover for about 456m by writing Code in Rover Markup Language to control it !! Recently, their biggest model, Claude Mythos came to spotlight for discovering bugs that were present for a long time on super stable and critical software (A 27 year old bug in OpenBSD for instance) and it has frightening cybersecurity capabilities to find vulnerabilities and scheme attacks. It is claimed to be so good that Anthropic decided to not release it for anybody except for a few companies developing world's critical digital infrastructure through Project Glasswing, so that they can use Mythos to find vulnerabilities in their software and fix it before bad actors can exploit them.
The Open Weight Models have evolved a long way too. A Chinese AI Startup, DeepSeek disrupted the market by releasing incredibly efficient, frontier level reasoning models like DeepSeek-R1 that rivaled top-tier proprietary models at a fraction of the training cost.Google has released Gemma family of models (Gemma 4 is the latest), which used the same research and technology as Gemini Series models, with great multimodal and multilingual capabilities in a small size. Even OpenAI eventually embraced this open movement and released GPT-OSS Family of Models in 2025.
The Next Frontier: From Chatbots to Multi-Agent Systems
Earlier, the standard way to use an LLM was through a chat interface. You type a prompt and the model generates text in response. While this was incredibly useful for getting our questions answered, writing, coding and brainstorming, the AI was still confined to a chat box. It couldn't take any action.
The next major leap was the transition from passive chatbots to active "agents". Early experimental systems like AutoGPT and BabyAGI in 2023 tried to give LLMs the ability to browse the web and string tasks together, but they were largely brittle and prone to getting stuck in loops. Then came agentic frameworks like LangGraph and CrewAI that allowed developers to create multi agent systems to perform useful work.
Fast forward to late 2025 and Early 2026, the agentic ecosystem has matured rapidly. Instead of just generating text, modern AI systems use tools and skills to interact directly with the digital world. A prime example is OpenClaw, a free, open source autonomous agent that went viral in early 2026. Instead of a standalone web app, OpenClaw runs entirely in the background on a local machine and connects to standard messaging apps. You can instruct it to manage your files, control APIs, read your emails or even reserve a table in a local restaurant and it can do it autonomously. I have spun up a personal assistant which is a custom fork of a famous project and it's been running on my server for several weeks now and its really amusing to watch it work. It does require some technical knowledge and lots of time with some resources to set it up, but not in a very distant future, all of us would have our very own Jarvis like personal virtual assistants running 24/7 and performing ton of tasks on our behalf.