How to Integrate Your Association’s Content into the AI Ecosystem 

In an age where technology is advancing at an unprecedented pace, Artificial Intelligence (AI) stands out as a transformative force. For associations, AI offers many opportunities to enhance member engagement, streamline operations, and deliver personalized experiences. However, the challenge lies in effectively integrating your association's unique content into the AI ecosystem. And spoiler alert... It’s not as difficult as you might think.  

We'll explore three primary approaches to achieve this integration, each with its own set of advantages and challenges. These methods range from using a vector store and prompt engineering to fine-tuning general-purpose Language Learning Models (LLMs) like GPT-3.5 or GPT-4, and even building a custom LLM from scratch. As we dive into these options, we'll consider factors like cost, technical expertise required, and the level of customization you can achieve. 

Importance of AI for Associations 

Before we tackle the technicalities, let's discuss why AI should be on your radar in the first place. For associations, AI can be a game-changer in the following ways: 

1. Member Engagement: AI-powered chatbots can answer member queries in real-time, providing immediate support and freeing up human resources for more complex tasks. AI can also boost engagement through gamification and elevate event experiences by simplifying the registration process. 

2. Data Analysis: AI algorithms can sift through large datasets to identify trends, helping you make data-driven decisions. For example, you could analyze member behavior to tailor your services more effectively. AI can enable you to see what your members are interested in and what they are not engaging with to ensure you’re focusing your efforts on high engagement areas. 

3. Content Curation: AI can help you personalize the content that your members see, enhancing their engagement and satisfaction with your services. 

4. Operational Efficiency: Automation powered by AI can streamline various administrative tasks, reducing overhead and allowing you to focus on strategic initiatives. AI can direct members to a course or instruct them to register for an event, so you don’t have to.  

The Dynamic Landscape of AI 

The world of AI is not static; it's evolving at a breakneck speed. Just six months ago, the cost and complexity of implementing AI solutions were prohibitive for most associations. However, the landscape has changed dramatically. Fine-tuning an LLM, for instance, has become significantly more affordable and accessible. This democratization of AI technology means that even smaller associations with limited budgets can now consider leveraging AI to enhance their operations and member services. 

Keep an eye on these trends - what may seem like a costly or complex endeavor today will likely become very manageable in the near future. A failure to embrace technological advances, including AI solutions, could leave your association lagging behind. It might sound like a lot of work up front, but many of these solutions are easier to employ than they seem and will drastically improve productivity. Let’s get into it... 

Approach 1: Vector Store and Prompt Engineering 

What is Vector Store and Prompt Engineering? 

A vector store is essentially a database that holds pre-processed information in a format that's easy for AI to understand. Prompt engineering, on the other hand, involves crafting specific questions or "prompts" that guide the AI in retrieving and presenting this information.  

How Does it Work? 

We’re sure your association has tons of articles, FAQs, and other content. Did you know that you can convert this content into a machine-readable format and store it in a vector store? With data in a vector store, an AI-powered chatbot can use carefully engineered prompts to pull relevant information for your members. It’s as simple as this quick workflow:  

1. Content Conversion: Your existing content is converted into a machine-readable format and stored in a vector store. 

2. Prompt Engineering: You create specific questions or prompts that your chatbot will use to interact with members. 

3. Member Interaction: When a member asks a question, the chatbot uses the engineered prompt to retrieve the relevant answer from the vector store. 

Pros and Cons

Pros: 

• Lower Cost: This approach is generally less expensive than other methods of content curation or retrieval. You're essentially using existing content within your association and making it machine-readable and easily accessible. 

• Ease of Implementation: With some basic technical know-how, you can set up a vector store and engineer prompts without the need for specialized AI expertise. YOU could quite literally do this on your own! 
• Quick Deployment: Because you're using existing content, you can get your AI-powered services up and running relatively quickly. 

Cons: 

• Limited Customization: Your AI's capabilities are restricted to the prompts you've engineered and the content in your vector store. To maximize the value of this solution for member engagement, it’s important to think critically about data structure and prompts.  
• Complex Queries: This approach may not handle intricate or nuanced questions well, as it relies on pre-defined prompts. However, it still provides the opportunity to collect important customer engagement data as it creates a record of customer queries.  

Best For: 

This method is particularly well-suited for small to medium-sized associations with limited budgets and technical resources. If you're looking to dip your toes into the AI waters without making a significant investment, this could be your best starting point.  

But... does this method exist yet? Indeed! Betty Bot is an AI chatbot trained on an association’s entire library of content. Once all that content (newsletters, webinars, journals, pdfs, and more) is in the vector store, Betty can reference it to interact with members in that association’s voice and expertise.  

By opting for a vector store and prompt engineering, you can make a relatively low-risk entry into the world of AI. It's a practical way to enhance member services without breaking the bank or requiring a team of AI specialists. 

Approach 2: Fine-Tuning General-Purpose LLMs  

What is Fine-Tuning? 

Fine-tuning is the process of adapting a pre-trained general-purpose LLM (like ChatGPT) to respond in a specific manner that aligns with your association's needs. So how is this different from the first approach? The first method we discussed provides the model with new information. Fine-tuning, on the other hand, allows the model to respond in a specific tone or format without teaching it anything new.  

How Does it Work? 

The fine-tuning process involves several steps: 

1. Model Selection: Choose an available general-purpose LLM like GPT-3.5 as your base model. It's worth noting that as of now, GPT-4 is not yet available for fine-tuning, although it's expected to be in the near future. 

2. Fine-Tuning: Use your association's specific tone, format, or other requirements to adapt the selected model. Reminder: this doesn't teach the model new information about your association, but it makes the model more likely to respond in a specific way that aligns with your organization’s needs.  

3. Deployment: Once fine-tuned, the model can be integrated into various applications, such as chatbots or content curation systems. 

Pros and Cons 

Pros: 

• Specific Response Framing: Fine-tuning allows you to tailor the LLM to respond in a specific tone, format, or manner that aligns with the voice of your association. 
• Reduced Prompt Complexity: With a fine-tuned model, you can potentially provide less information in the prompt while still receiving the desired response. 
• Moderate Cost: The cost of fine-tuning has decreased significantly, making it a viable option for plenty associations. 

Cons: 

• Technical Expertise Required: While less daunting than building a custom LLM, fine-tuning still requires some level of technical expertise. 
• Limited to Available Models: As of now, only certain models like GPT-3.5 are available for fine-tuning. 

Best For: 

This approach is ideal for medium to large associations that have a moderate budget and some technical resources. If you're looking to make the model respond in a specific way without necessarily teaching it new information, fine-tuning could be the right choice for you. It might be helpful to think of this option as the middle tier to AI programming. 
 

Approach 3: Building a Custom LLM 

What is a Custom LLM? 

A custom LLM (like Llama 2) is a language model that you build from the ground up, tailored specifically for your association's needs. Unlike fine-tuning an existing model, this approach involves training a model on both public data and your organization's unique content. The result is a completely customized solution for your organization and members.  

How Does it Work? 

The process of building a custom LLM involves several steps: 

1. Data Collection: Gather a comprehensive dataset that includes both public information and your association's content. 

2. Model Selection: Choose an open-source model framework like Llama 2 as your starting point. 

3. Training: Use your dataset to train the model. This is a resource-intensive step that requires specialized hardware and expertise. 

4. Testing and Iteration: After the initial training, you’ll want to test the model rigorously and make necessary adjustments. 

5. Deployment: Once the model is trained and tested, integrate it into your desired applications. 

Pros and Cons 

Pros: 

• Highest Level of Customization: A custom LLM offers unparalleled customization, allowing you to create a highly specialized AI tool. 

• Best Performance: Custom models generally offer the best performance in terms of accuracy and relevance. 
• Ownership: You own the model and can adapt it as you see fit, without relying on third-party providers. 

Cons: 

• High Cost: Building a custom LLM is the most expensive option, requiring significant investment in both hardware and expertise. 
• Technical Complexity: This approach requires a high level of technical skill and resources. 

Best For: 

Custom LLMs are best suited for large associations with substantial budgets and technical resources. If you're looking for the highest level of customization and are willing to make a significant investment, this is the approach for you. 

Alternate Methods and Conclusion 

While the three approaches outlined above are the most common, there are alternative methods worth considering, such as API-based solutions, hybrid models, or even outsourcing to AI consultancies. 

The rapidly evolving landscape of AI offers a range of options for associations looking to integrate their unique content. The first approach allows you to train the model on your association’s content. The second approach allows you to fine-tune an existing model to make it respond in a way that fits your association’s needs. The third approach does all that and more to create a custom-built solution for your organization.  

Whether you're a small association just starting with AI or a large organization looking to maximize customization, there's an approach that fits your needs and resources. If you aren’t sure exactly where to start, we recommend getting your hands dirty! The only way to find the perfect AI solution for your association is to test out the models that already exist to decipher which capabilities are most important to your organization.  

Try out ChatGPT, Claude, or Bard when you have some free time. Or better yet, try out an AI model built exclusively for associations: Betty Bot. You can try Betty out for yourself right here on the Sidecar website by creating a free account!