How to Use Generative AI Without Building a Large Language Model (LLM)?

Generative AI – a novel computing paradigm that has profound implications for the world of business (and beyond) – from developing compelling content for marketing e-mails to generating gripping videos and writing code with high efficacy to empowering chatbots to provide highly accurate responses, the groundbreaking technological development is paving the way for a productivity revolution. Therefore, it didn’t come as a surprise when Forrester predicted that the adoption of generative AI tools would record a staggering average annual growth rate of 36% up to 2030.

A crucial requirement of using generative AI is developing a robust Large Language Model (LLM). An LLM is a deep-learning algorithm that utilizes Natural Language Processing (NLP) capabilities to deliver human-like responses. Companies use massive sets of data to train LLMs, which enable the models to acquire an in-depth understanding of the overall context of information, allowing the language models to analyze, make rational inferences and draw the correct conclusions. However, building and training an LLM consumes a lot of time and is fraught with high expenditure and risk to the security of sensitive business information.

Today, we’ll see how you can overcome this problem to make the best use of generative AI to propel the growth of your business. But first, let’s look at various ways you can use LLMs and understand their benefits and drawbacks.

What Are the Different Ways in Which You Can Leverage an LLM?

You can choose any of the following three ways to utilize an LLM to harness the immense potential of generative AI for your organization.

1. Imparting Training to an LLM Built from the Scratch

When you train your custom-built LLM, you can choose its architecture and chart the training process based on your unique business needs. You can also use data sets pertaining to your industry to train the LLM.

Key Benefit

This approach will enable the LLM to produce super-accurate results for your industry-specific use cases, as the model will be grounded solidly in relevant domain-related data.

Major Drawbacks

• It takes several weeks or even a few months to implement this approach.

• A lot of computer hardware is needed to train the LLM well.

• A team of Machine Learning (ML) and NLP experts is required to deliver the training.

• There could be problems with data access and security; remember, the more the data fed into an LLM, the better the results will be. In most cases, the Principle of Least Privilege (PoLP) governs the use of data – the principle calls for companies to give users access to only the data they need to perform their job duties. Put another way, the PoLP is based on the notion that the less the data, the better. It’s hard to make the two contradicting principles work together.

• Most importantly, training the language model would be very expensive.

2. Customizing a Pre-trained, Open-source Model

Another way to use LLMs is to leverage an open-source language model pre-trained on massive data sets and tweak it to suit your specific requirements.

Key Benefit

You can reduce the time and cost incurred to use the LLM in a big way compared to the first approach.

Major Drawbacks

• A team of ML and NLP specialists is needed to customize the pre-trained language model.

• Data safety can be an issue, albeit to a much lesser extent than training an LLM from ground zero.

3. Harnessing Existing LLMs Through Application Programming Interfaces (APIs)

The third and most popular way is to utilize existing language models such as OpenAI, Anthropic, Google etc.

Key Benefits

• Little time and money are needed to train the model.

• Specialists in ML and NLP are not required.

• Data safety problems are non-existent; you can dynamically build the prompt into a user’s workflow, ensuring usage of only the data accessible to the user.

Major Drawback

The accuracy of results will take a severe beating as the LLM isn't trained using specific contextual and organization-related data.

You can overcome this challenge using a novel training method called in-context learning. Let’s now proceed to see how this method works.

What Is In-context Learning and How Does It Enhance the Quality of the Output of an LLM?

In-context learning enables a language model with the ability to comprehend and produce high-quality results by feeding contextual data. Consider the two prompts given below.

Prompt 1 (Not Fed with Organizational Data)

Write an introductory e-mail to the KENDRI Defense CEO

Prompt 2 (Fed with Organizational Data)

You are Nathan Harris, Account Executive at Manklot Alloys

Write an introductory e-mail to Ron Nool, the CEO of KENDRI Defense Inc.

KENDRI Defense has been a client since 2020.

It buys the following super-high-tensile metal products: Champ, Pinnacle, Royale, Ace.

Here is a list of KENDRI Defense purchases:

2023: USD 1,500,000

2022: USD 1,275,352

2021: USD 1,119,422

The model utilized to generate responses using the two prompts listed above doesn’t have any relevant organizational information. Therefore, the result produced by Prompt 1 will be very generic. But, because we’ve added client information to Prompt 2, the LLM understands the context and generates a more accurate, personalized result, although it wasn’t trained on that information.

The more grounding information you incorporate in the prompt, the more precise the results will be. However, users may not always be able to provide detailed grounding information for each request manually.

Salesforce Prompt Builder helps you overcome this problem by enabling you to write prompts thoroughly grounded in your organizational information. The novel application allows you to develop prompt templates easily, thanks to its intuitive graphical interface. You can tie placeholder fields to dynamic information accessible through flows, Data Cloud, Apex calls or API calls to produce highly accurate, useful outputs.

However, incorporating organizational data in the prompt can lead to data security issues – when you pass confidential business data to an API provider for training an LLM, there is no guarantee that the data will be safe.

You can resolve this problem with high efficacy using Einstein Trust Layer, a robust, secure AI architecture built natively into the Salesforce Einstein 1 platform. Let’s see how.

How Does Einstein Trust Layer Help Protect Sensitive Data Used to Train LLMs Through APIs?

Provides a Secure Data Gateway

You can leverage Einstein Trust Layer's secure data gateway to access the language model without making direct API calls. The gateway provides excellent support to different LLM providers and helps abstract the differences between them. You can even plug in your own LLM or an open-source model customized to your specific needs.

Masks Data to Ensure Regulatory Compliance

Before sending a request to an LLM provider, Einstein Trust Layer subjects the request to a procedure that includes masking the data and replacing confidential personal information with fake data to comply with data safety norms.

Ensures Zero Retention of Data

Salesforce has entered into zero data retention agreements with LLM providers. The agreements will prohibit the latter from storing and utilizing information sent by Salesforce to train their language models, thereby preventing misuse of the data.

Helps De-mask Data, Detects Toxicity and Sets Up an Audit Trail

When the LLM delivers the output, it’s subjected to a series of steps that include de-masking the data, detecting toxicity and creating an audit trail. De-masking replaces the fake data with the real data, while toxicity detection checks for and flags any objectionable content in the output. The creation of the audit trail involves logging the entire process comprehensively.

We’ll now learn more about the Salesforce Einstein 1 platform.

Salesforce Einstein 1 Platform – Helping Unleash the Tremendous Power of LLMs

The Salesforce Einstein 1 platform facilitates the seamless abstraction of complex LLMs. The platform is used to power the next generation of Salesforce applications and allows you to develop LLM-based tools without hassles. Salesforce Einstein 1 supports different approaches of training LLMs that we’ve seen earlier (training your own model from scratch, customizing a pre-trained, open-source LLM and leveraging existing models through APIs). Thus, it allows you to make the best use of language models to harness the immense potential of generative AI to drive business success.

As you can see, you don’t need to spend considerable time, money and effort to build an LLM from scratch to use generative AI tools with high efficacy. Instead, you can use the Einstein Trust Layer built into the Salesforce 1 platform to use existing LLMs through APIs to get the desired results. At Solunus, we help you leverage cutting-edge solutions from Salesforce to utilize generative AI to meet your business goals. Our team of seasoned technology professionals follows a proven ‘needs-first’ approach to enable you to develop a solid strategy to use the novel AI paradigm to meet your goals.

Hope you liked this post. How do you use generative AI in your business? We’d love to know.

About Solunus

Solunus is a leading Salesforce consulting company, based in Dallas, TX, USA. Our proven ‘needs-first’ approach coupled with our unrivaled expertise of the Salesforce platform enables us to provide the perfect solution to help you deliver delightful services to customers and achieve rapid growth.