Why You Need AI Guardrails for Your Content Standards

The rising demand for quality content

Did you know that 88% of respondents in a survey conducted by IBM, Adobe, and AWS*, report that the demand for content has doubled in the last two years, and two-thirds say that the demand will increase five to twenty times in the next two years? It’s any wonder that the demand for content continues to grow, especially as technological developments like Large Language Models (LLMs) make it exponentially easier to accelerate content creation. 

In our recent webinar “Enterprise-Grade AI Guardrails”, we covered important terminology in the generative AI landscape, trends in enterprise generative AI, some real word applications, and best practices supported by Acrolinx. This blog summarizes the key points in the webinar that we feel are important to any enterprise looking to integrate LLM-generated text into their content strategy. 

We asked you about how you use content to ground your LLMs, and talked about how AI guardrails for your content are critical for LLM-generated content. Not only for content quality assurance at scale, but clear, compliant AI-generated content that meets regulatory standards relevant to your industry. 

We’re well and truly past the era where the saying “there’s no bad content” reigns. In the era of AI, you need enterprise-wide content guardrails that protect your business — and your customer experience. 

Revolutionizing content creation with the content supply chain 

In this new world, organizations are reconceptualizing how they think about content. Much like manufacturing equipment or cars, to scale content production you need to come up with new and different ways to improve the process to ensure you’re not compromising quality. 

Manufacturing calls that process supply chain thinking. As we move towards a world where both human- and AI-generated content exist, organizations are starting to think of content like a supply chain instead of something that’s hand-crafted on a case-by-case basis. 

Content that’s perceived as a valuable business asset changes the way an organization approaches its creation and management. People have traditionally thought about content through the lens of; who is the audience, who uses the content? Today, we also have to think about machines as content consumers. We don’t just write for people, we now also write for machines. What insights do you have into your content that informs and drives your LLM strategies? 

Given that AI takes content production to a whole new level, it’s time for a new way to create and manage content. Best practices and content governance are the missing links to successfully adding AI into your content supply chain. 

Why your style guide needs AI content guardrails 

If you’re like many Acrolinx customers and work in a large enterprise, you probably have a large brand style guide. It’s usually hundreds of pages long, designed to keep your content aligned with your brand, business goals, and compliance requirements. Ask yourself: 

1. Do our standards reflected in our style guide matter?
2. Does it matter that as content production increases exponentially, that it’s inclusive, that it meets your local regulatory requirements, the tone is right, or that the terminology is consistent across the entire customer journey? 

Every supply chain includes a step for quality assurance — and so should your content supply chain! If you answered yes to either of the questions above, you need AI content guardrails to maintain those standards. Guardrails are governance in action; they’re the content quality assurance steps in your content supply chain. 

A style guide goes well beyond simply staying on-brand. It includes how well your content meets industry regulations for compliant terminology and formatting. In the realm of compliance, non-adherence to those standards introduces business risk in the form of:

1. Legal non-compliance 
2. Brand degradation
3. A poor customer experience

Consider one Acrolinx customer who had their website shut down in one of their target markets for using claims-based language. How do you avoid that problem before it happens, and triage it if it does happen? Guardrails that apply your style guide enterprise-wide, are the solution. 

Understanding the LLM models powering AI content

LLM solutions are proliferating faster than we can think about how to best prompt them to get what we want out of them! There are different types of models, and an enterprise might test more than one. OpenAI is however, the dominant LLM in production today, with 79% of OpenAIs customers coming from the Microsoft Azure-OpenAI partnership. 

Open-source models are the most popular, but just under half of the respondents in the report cited earlier, shows that when the performance of open-source models improves, organizations will consider switching to open source. Why? Because it allows for control and customization. Let’s explore those further:

• Control: Enterprises have sensitive data that needs to stay secure. If you use an open-source model in your own infrastructure, it gives you greater control over the level of security. And the accuracy of LLM-generated outputs.  
• Customization: As they deploy different models for different use cases, organizations want the ability to fine-tune these models to personalize outputs for different content types or target audiences, without compromising on alignment to their bespoke content standards and enterprise style guide. 

Leaders looking to invest in LLMs are looking for the control over risk and to get the right answers out of their LLM. But what if you want to train or ground your LLM for specific use cases? Well you probably want to train it on a narrower data set (for example, for code creation, medical applications, or customer support conversations). Let’s explore the different definitions that describe techniques that take your content, and use it to ground or fine-tune your LLM, to produce different desired outputs. They’re designed to:

• Anchors model responses to specific information.
• Enhances the trustworthiness and applicability of the generated content.
• Reduces model hallucinations, which are instances where the model generates content that isn’t factual.

Fine-tuning for targeted content creation

Fine-tuning involves adjusting an already pre-trained model on a new, typically smaller, dataset that’s specific to a particular task or domain. It works by adjusting a pre-trained LLM to a specific task by further training the model on a narrower dataset for a specific application – like a customer service chatbot, or medical research. 

Enhancing AI accuracy with Retrieval-Augmented Generation (RAG)

RAG is the process of optimizing the output of an LLM, so it references an authoritative knowledge base outside of its training data sources before generating a response.

RAG is the most effective technique for LLM grounding. RAG enriches LLMs with your trusted, up-to-date business data. It improves the relevance and reliability of LLM responses by adding a data retrieval stage. Usually, that happens via a prompt, to the response generation process. 

Implementing AI guardrails for content creation: Best practices

Let’s explore how companies implement grounding techniques, specifically in relation to upholding content standards. We asked our webinar audience: Is the content you’re responsible for used to fine-tune your company’s LLM today? 

• 40% answered “yes”
• 31% answered “no”
• 29% answered “I don’t know”

Companies have the choice to implement quality assurance guardrails at many different stages of the LLM-grounding or fine-tuning process. Best practices for implementing guardrails include:

• Quality check before fine tuning: Checking the current quality and attributes of the content you’re using to train your LLM with before using it as data for fine-tuning. 
• Quality check on generated completions: Checking that the content generated by LLMs meets your standards and expectations. How well does it align with your style guide and where does it deviate? Measuring content quality at this stage is important to give feedback to your LLM about where it’s generating inaccuracies. 
• Quality check in workflows: Integrating content quality checks into the human-powered creation process. Such as automated quality gates at different stages in the content supply chain, to block non-compliant content from being published. 
• Quality check of published content: Auditing and analyzing the quality of content that’s already published, to ensure it stays up to date and aligned with your standards. 

Acrolinx use cases of AI content guardrails 

Acrolinx is an AI-powered content governance software that captures and digitizes your style guide to make your writing standards, standard. It governs new and existing content written by people and generative AI. Let’s look at two use cases of implementing guardrails to successfully deploy generative AI in your enterprise with reduced risk, and greater efficiency. 

Ensuring quality in AI-generated completions 

One of our customers lets users fill out a form that contains some simple questions, and has generative AI complete the answers to generate an article based on this input. With the user input as “prompts”, the LLM builds the article as a so-called generated completion. Acrolinx then automatically quality-assures the AI-generated article to make sure it’s aligned to the organization’s standards for accuracy, tone, voice, style, terminology, messaging, and compliance to any content-specific industry standards. They use Acrolinx guardrails to ensure that AI-generated content is doing the job it’s intended to do.

Integrating quality checks into workflows 

Everyone in your business creates content. From emails to documentation, everyone writes as part of their role. Below you’ll see how Acrolinx integrates into wherever your teams write. It highlights problematic issues in content and you can prompt it to suggest replacement wording that’s already aligned to your style guide. It boosts writer productivity and efficiency as it relieves writers from having to creatively rewrite content while applying your style guide to their text. 

Imagine the implications for teams reviewing legal documentation, or large, text-heavy documents that require precise wording that prioritize clarity and the correct terminology for your business and industry. Reviews and approvals become faster, and time-to-publication increases. 

Balancing AI benefits and risks with guardrails

To summarize, Acrolinx provides improvement suggestions wherever you write on how to keep your content clear, consistent, and compliant. You can use Get Suggestions to generate content that’s grounded on your style guide using Retrieval-Augmented Generation (‘RAG’), where Acrolinx then rechecks that output to ensure alignment to your standards, scored, and then depending on the score, either resent to humans to improve or permitted to be published. 

Continuously checking AI-generated content is important to make sure that the AI model you’re using doesn’t gradually “drift” into the domain of being off-brand, as these models are likely to do, if they don’t have a guardrail to “bump into” to keep it aligned to your style guide, business goals, and audience expectations. 

Acrolinx allows you to leverage the benefits of AI, with risk management in the form of content governance guardrails. 

Whether your company has written 100,000 words or billions (like our customer Microsoft), Acrolinx makes sure each one reflects your style guide. Customers enjoy massive efficiency gains without sacrificing standards through AI-powered live writing assistance, automated reviews and quality gates, and analytics comparing content quality with performance.

With an LLM infrastructure anchored in Azure AI, Acrolinx guarantees scale, future-readiness, and uncompromising safety and security. Born out of the German Research Center for Artificial Intelligence (DFKI), AI runs deep in Acrolinx’s DNA.

*Source: The Revolutionary Content Supply Chain, IBM, Adobe, AWS, March 2024