Why Selecting Language Models is So Hard

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Selecting the right language model is crucial for developing cutting-edge natural language Generative AI applications. Their unique attributes can significantly impact the success of your implementation. But the selection process can be daunting, given the variety of models, each with their own strengths and weaknesses. This complexity makes integrating LMs effectively in an application all the more challenging. In this post, we’ll explore the intricacies of choosing the right language models and how Expero simplifies this process, empowering you to make informed decisions that drive successful results.

So many options to choose from!

One of the most overwhelming aspects of choosing LMs is the sheer variety. Beyond the growing number of LMs to choose from, consider some of the factors that set LMs apart from each other:

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• Training data sets
• Model architectures
• Context sizes
• Number of parameters
• Proprietary or open-source 
• Training specifications
• Regulatory requirements

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In short, no two LMs are the same, and their model outputs will vary in terms of accuracy, quality, speed, length and reasoning. In many cases, models are trained or fine-tuned for a particular purpose. Take the model Med-PaLM, which is trained on a corpus of medical data to provide high quality answers to medical questions. Simply put, not every model is cut out for every task.

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Developers also have to grapple with the black-box nature of LMs. A slight change in the model’s input, i.e. a prompt, can result in a considerable dip in performance, and now your perfectly tuned workflow fails 20% more often. Some models can handle this subtle balance of accuracy and precision much better than others.

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As if selecting the right LM isn’t complicated enough, model providers are constantly phasing out “old” models in favor of new ones. Many of these newer models are optimized to improve performance of certain tasks, but this poses a particular challenge. For instance, even if version 1 of Model A handles a nuance in the prompt well, version 2 might fail to pick up on that nuance, even if version 2 is considered “better” than version 1 in a variety of ways. This means that teams have to test their workflows each time a model is changed. Choosing a model isn’t necessarily a one-and-done affair.

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Another issue impacting model selection is the proprietary status of models and accessibility in the cloud. Some clients require their models to be on prem, while others are required to work with a specific cloud provider. Adding to that complexity, not all models offered by these providers go through the same rigorous processes to ensure they are trained to produce “safe” results. Thus, many teams must commit to a lengthy compliance process to approve models for use. These factors alone can have a huge impact on which models our clients can choose from.

How Expero Solutions Can Help

Expero has worked with an array of LMs in a variety of settings and cloud environments, ranging from Azure’s offerings to Google AI, AWS, Hugging Face and even on-premise solutions. We’re well versed in all the different factors making LM selection difficult, and we know just what to do to alleviate those concerns. Our approach is driven by two key principles:

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1. Understanding user intent
2. Ensuring that LM selection is easy and flexible

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Before we dive into language model selection, our first step is to deeply engage with our clients and understand what their users truly need. This key step helps us shape Generative AI to best serve and elevate the user experience. I highly recommend reading our previous blog on “Why Your Approach to Gen. AI might be wrong” for more insights on why understanding user intent is pivotal to creating effective Generative AI applications. Establishing this clarity early on is crucial for making informed decisions and aligning AI capabilities with actual user goals.

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We also know that each client has specific constraints–whether that be platform compatibility or model restrictions. That’s why our engagement process includes a deep dive into these requirements, to understand any Generative AI limitations up front. From there, we carefully vet our options to select the best models for the job. While engaging with our client is an essential process, it’s only a portion of what we bring to the table. 

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The other key principle underlying our approach is ensuring LM selection is a simple, flexible process. That’s where we leverage Jetpack, our highly customizable advanced generative framework built to understand user intent and streamline the Generative AI development process. Jetpack represents each logical workflow as a series of modular steps. LMs are configured at the smallest unit level to allow fine-grained tuning of Generative AI workflows and results.

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Jetpack’s modularity also limits the “cognitive load” imposed on the LM, leading to simpler, more concise prompts and outputs. By leveraging LMs in this way, we provide our clients with more flexibility in models to choose from. For instance, our approach means:

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• Less need for high reasoning models
• Less need for huge context window
• Less reliance on vendor specific implementations (e.g. function calling)

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A fundamental aspect of the LM selection process is the ability to switch between different LMs. Jetpack abstracts the model configuration from the logical workflows, making the process of switching out LMs, in any environment, a breeze. Jetpack also provides a testing suite to validate the performance of each LM in tandem (end-to-end) or as a single unit. These results allow us to make data-driven decisions about which models work best for various tasks.

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Expero’s approach to selecting the best models is grounded in the principles that drive our success: aligning with each client's unique needs, simplifying the LM selection process, and delivering impactful results. Jetpack streamlines model selection, and enables data-driven decisions that set our clients up for success from the start. 

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Take the Next Step with Expero

As a leader in developing successful financial solutions with GenAI we understand the critical role technology plays in delivering exceptional client experiences and driving growth. Whether your team is just beginning the GenAI journey or if your team has key use cases defined for how GenAI can drive revenue or cut costs, Expero’s experts can help you bridge this gap.

We combine deep financial expertise with advanced technical skills and a strong focus on user experience to deliver transformative wealth management solutions. Our unique hybrid approach transcends the traditional build-versus-buy dilemma by blending custom development with our CONNECTED Finance Accelerators—a suite of advanced, configurable components.

Key Benefits for Your Firm:

• Accelerated Time to Market: Rapidly launch new features using our pre-built components, allowing you to focus on delivering immediate business impact.
• Enhanced Client Engagement: Support both self-directed investors and collaborative advisor relationships with personalized insights, guided investment tools, and advanced analytics to deepen client relationships and drive loyalty.
• Flexible and Scalable Solutions: Our modular approach adapts easily to changing market conditions and regulatory requirements, ensuring your solutions evolve with your needs.
• Incremental Value Capture: Grow solutions at a pace that continually captures value, focusing on strategy and innovation rather than accumulating tech debt.