NILG.AI

Large Language Models (LLMs) are THE hot topic of the year. If the name Large Language Models sounds unfamiliar to you, I’m pretty sure you’ve heard of ChatGPT, OpenAI, and Bard. People who don’t know how to code have gained access to a tool that allows them to build Proof of Concepts for ideas they’ve been meaning to test for years but haven’t had the capacity to do so. 

In this blog post, you will learn about typical use cases of LLMs in the industry – and what you need to be careful about when using them.

NOTE: A human entirely wrote this blogpost – yes, humans writing content is still a thing! If you like this human’s ideas, book a meeting!

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Copywriting Assistant with Large Language Models

Using Large Language Models to speed up writing blog posts, sales pitches, and ads is a widespread use case where these models excel. You can feed the tone of your content or specific keywords you want to mention in the generated text – and you’ll be left with a canvas that you can edit to your liking, saving you hours of work.

Risks:

• Lack of most up-to-date data: LLMs are trained on data until a specific time so they won’t be aware of the most recent trends. As a copywriter, you should be sensitive to this and should iterate.  Note that you can include this data as context in your prompt to mitigate this. 
• Distinct style: LLMs have a typical way of writing: to improve the style, you can feed the model previous content written by you to make it more similar to your writing style.

Learning resources:

• “500 Prompts for Copywriting”

GDPR-Compliant Data Generation with Large Language Models

In a world of increasing regulations in AI, generative technologies can be used to create data for faster proof of concepts, avoiding training models from scratch (model cold start). 

Picture this: you are managing the customer service department and can train a model for text classification without using any personal user data, by generating various categories of complaints with LLMs! 

Risks:

• Generated data is biased towards what is commonly seen on the internet: if you try to generate medical imaging data, you won’t have something very accurate and diverse! 

Resources:

• DALL-E Prompt Book

Actionable Knowledge Base using Large Language Models

Being able to answer questions based on a knowledge base saves tons of time when onboarding a team member or when you’re trying to search for information in multiple unstructured documents. 

Risks:

• LLMs might hallucinate information in some cases, when the answer is not clearly defined. 

Resources:

• Basics of building a knowledge base
• Intercom’s FIN

Coding Assistant

If you’re a coder – most likely you’ve used LLMs to get you unstuck on a piece of code or to learn a new language. This is a good complement to checking Stack Overflow for someone that might have had the same issue as you – but in this case, you won’t be left without a reply!

Be on the lookout for the code you submit here – a recent article has stated that Samsung workers accidentally leaked trade secrets via ChatGPT!

Risks:

• Non-compiling code: There’s no guarantee that the generated code will 
• Knowledge obsolescence: Junior coders end up being too dependent on these tools (but remember the last time you did a difficult mathematical operation by hand?) 

Resources:

• Github Copilot

Conclusion

Large Language Models are here to stay. Every week, there’s improvements and new tools built on top of LLMs – but we are just now starting to understand how to build products based on it.

Besides the obvious risks – we can’t forget about the extra dependency you’re creating for your business on OpenAI’s technologies. Any legal change (see Italy’s ban on ChatGPT) can completely break your business if you create a 100% dependency. Diversification of the used technologies is always good for mitigating risk. You should consider open-source alternatives for LLMs, if needed!

If you’re interested in using this new technology in your business, contact us!

Do you want to further discuss this idea?

Book a meeting with Paulo Maia

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