Navigating the LLM Landscape at Yellow.ai

The LLM Revolution and the Need for Change

The digital age introduced the world to a series of innovations, with Large Language Models (LLMs) like Chat GPT standing out prominently. These models, with their vast knowledge base and intricate design, transformed industries overnight. However, as we at Yellow.ai meticulously analyzed their integration into real-world applications, potential hurdles became evident. Imagine a customer support scenario where a customer is on a call with a voice-based assistant, driven by LLMs, With the current state-of-the-art OpenAI’s gpt4 takes its own sweet time to respond. This gives a very bad experience to the end user who is waiting for help. Beyond just the user experience, the cost of inference is also very high for these gigantic models. With increasing reliance on API-based solutions, the unpredictability of costs and response times made it clear: a paradigm shift was essential.

An Orthogonal Shift: Task-Specific Fine-Tuning

While the potential of LLMs like Llama was widely recognized, there was no comprehensive knowledge about how to fine-tune them effectively. At Yellow.ai, we discerned a unique opportunity. Instead of a generalized approach, why not hone our efforts on tasks — the very core of any conversation? This perspective shifted our focus towards models like the 7 billion, 13 billion, and 33 billion parameter versions of Llama. With these distinct models, each was meticulously crafted for specific conversational tasks. Under this framework, our ML/Data Science team invested their expertise, ensuring each model was perfectly calibrated for its designated role in the conversational ai pipeline.

Efficiency, Cost & Accuracy

In our quest for superior conversational AI, merely redesigning the fine-tuning process wasn't enough. We ventured further into the realm of technological innovations. Quantization techniques became our trusted ally in this journey, offering us a dual advantage: enhanced model performance and substantial cost savings. This restructuring allowed us to create a tiered system. Smaller, more efficient models tackled foundational tasks, ensuring swift and accurate responses. This, in turn, significantly reduced the chances of hallucinations.

The Road Ahead

While we continue our innovations, we humbly acknowledge the powerhouses of the LLM world. Models such as GPT-4 are in a league of their own, offering unmatched insights and depth. However, with our revamped strategy, their deployment is more strategic. By ensuring they are used where their intricate understanding is indispensable, we balance computational prowess with practicality. The advent of LLMs has opened doors we hadn't imagined.

Our dream of realizing a staggering 90% automation rate in customer support no longer seems a distant star but a tangible reality. At Yellow.ai, we're not just embracing the future; we're shaping it.