Why ChatGPT and Friends Won't Be Stealing Your Job Anytime Soon: Navigating the Hurdles of Large Language Models for Enterprise Use Cases.

It’s almost been a year since ChatGPT (Chat Generative Pre-trained Transformer) came out and in the age of advanced language models, LLM’s have been everywhere and nowhere, the buzz around Large Language Models (LLMs) has sparked curiosity but integrating them into everyday business comes with its own set of puzzles. There have been quite a few enquiries about these scenarios off late. Though the seemingly straightforward adoption of LLMs might not be as smooth as it appears.

Nascent Business cases with LLMs:

1.??? Translation Tricks:

·???????? LLMs are great at translating languages, but when it comes to specific topics, specialized translation models might outshine them.

2.??? Data analysing Magic:

·???????? LLMs can turn messy data into organized information, making it easier to analyze and understand. Quite a few use cases can be based on this like pattern detection, coding support, reporting, monitoring, data conversion etc.

3.??? Q&A Showdown:

·???????? LLMs are champs at answering questions based on what they've learned, but they might struggle with tricky or very specific queries.

4.??? Summarizing Superpower:

·???????? LLMs are like superheroes, summarizing long texts into short, clear versions, saving you from information overload.

5.??? Customer Support Sidekick:

·???????? LLMs make great allies in customer support, helping with information, FAQs, and making virtual assistants more helpful.

Simple Use cases aside, LLM’s face multiple challenges to trickle down from drawing boards to actual business mandates to use them for complex business cases.

Bumps in the Road for Business Integration:

1.??? Getting the Facts Straight:

·???????? LLMs may not always have the latest info, and sometimes, they might even make things up when data is missing. This could be a problem for businesses that need accurate and traceable information. Let’s face it, no model can be 100% accurate, business will require traceability for the inaccurate cases. Ever tried asking ChatGPT why it gave an incorrect response 2 days back.

2.??? Understanding the Business Lingo:

·???????? In tricky business situations, LLMs might get confused with specific terms or industry jargon, leading to incorrect or off-target responses.

3.??? Privacy Woes:

·???????? When LLMs deal with sensitive business data, privacy becomes a big concern. There's a risk of revealing confidential info, not just in using but also in fine tuning, especially if third parties are involved in training and model hosting.

4.??? The Bias Bug:

·???????? LLMs carry biases from their training, which could mean unfair or discriminatory responses. In areas like hiring or decision-making, this is a big deal.

5.??? Access Drama:

·???????? LLMs don't always come with built-in access controls, making it tricky to limit who sees certain data. This can be a headache for security and compliance.

6.??? Cost Crunch:

·???????? Dealing with LLMs can be heavy on resources and money. Constant training and fine tuning, maintenance, and adjustments to new situations can add up.

7.??? Regulation flux:

·???????? AI regulations are rapidly changing as the field matures, no business wants to put a lot of effort to put things in production, only to be later told by their compliance teams to roll back.

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We may ask ourselves, where is the AI era which Generative AI was supposed to bring, because the current LLM’s are far from it, at least for Enterprise business cases.

To untangle these challenges, a new architectural approach called Retrieval Augmented Generation (RAG) shows promise. It blends fetching information with creating responses, making LLMs a bit more practical for business use. As businesses step through the twists and turns of LLM integration, solutions like RAG offer a simple yet effective way to make these language models work better in the real world of business data.

Here’s hoping the next super intelligent generative AI will be able to solve most of these issues.

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To be continued.