Evolution of GenAI-based Solutions

Experimenting with GenAI, specifically with Large Language Models (LLM), was a thing of 2023. Drafting marketing content, crafting internal policies, polishing emails, and finding bugs in the code were some of GenAI's early use cases. Concerns over data privacy, authenticity of generated content, and hallucinatory responses spurred the development of robust AI policies within enterprises, emphasizing the need for strategic oversight and transparency in AI adoption.

Proliferation of GenAI across enterprise

With growing awareness of GenAI’s potential, business and technology leaders embarked on a journey to integrate AI into various facets of their operations - improving digital engagement with customers, streamlining internal processes, and improving developer productivity. This surge in interest prompted the establishment of Centers of Excellence (CoEs), with stakeholders from legal, security, lines of business, and IT domains, serving as hubs for knowledge dissemination, best practices, and tool landscape. As organizations grappled with the myriad possibilities GenAI presented, the importance of identifying and prioritizing business use cases based on their transformative impact emerged as a guiding principle.

Early implementations of GenAI

By mid-2023, many organizations had taken concrete steps towards integrating GenAI into their workflows. One such example is the development of GenAI-based applications to assist sales teams in summarizing client meeting notes and formatting them for playbacks. Leveraging prompt engineering techniques and OpenAI’s APIs, organizations reduced the barrier of disclosing proprietary information and hallucination. With the advent of RAG (Retrieval-Augmented Generation) architectures, organizations could enrich LLM responses with their domain-specific data. However, RAG architectures based on SaaS-based vector databases and OpenAI APIs may become expensive. Additionally, organizations may not be comfortable sharing their data with vendors outside of their trusted partners.

Walled Garden Approach to GenAI

As organizations sought greater control over their AI infrastructure and data privacy, a shift towards localized deployment of LLMs emerged. Solutions like Llama2, Falcon, and PaLM offered organizations the flexibility to host AI models on-prem or within trusted cloud environments, mitigating data sovereignty and compliance concerns. Organizations could seamlessly deploy RAG-based architectures by leveraging partner cloud vendors like Amazon Bedrock while benefiting from native cloud capabilities. The adoption of supervised fine-tuning (SFT) techniques further empowered organizations to tailor LLMs to their specific domain requirements, striking a balance between model performance and data privacy.

What’s next?

Looking ahead, the focus is shifting toward optimizing LLMs using a combination of techniques such as Supervised Fine-Tuning, RAG, Single-shot, Few-shot prompting, and Direct Preference Optimization.? In the forthcoming articles, we will delve deeper into the design considerations and challenges of implementing these techniques in real-world use cases.