The Challenges of Programming Specific Behaviors in GPTs: A Case Study with Eva, the AI Scheduling Agent for Healthier Plate

Programming conversational behavior for generative pre-trained transformers (GPTs) is no small feat. These models, while sophisticated in their language capabilities, often face challenges in achieving nuanced and targeted behaviors, especially in real-world applications. From ethical considerations to data limitations, the difficulties are numerous. In this post, we’ll explore the complexities of programming specific behaviors into GPTs, using a real-world example: Eva, a scheduling assistant developed for Healthier Plate, a nutrition and wellness service. Eva's primary role is to assist in booking appointments by responding to SMS messages and emails, providing a glimpse into both the promise and challenges of programming GPTs for specialized tasks.

Introduction to Behavioral Programming for GPTs

GPT models like ChatGPT are designed to predict and generate human-like responses based on large datasets. The goal of programming specific behaviors is to create models that can interact in a way that aligns with specific contexts, tones, and business needs. However, due to GPTs' reliance on large, unfiltered data sets and complex linguistic patterns, creating a model that consistently adheres to a particular behavior—such as empathy, professionalism, or tone control—is a technical challenge. These challenges are amplified in applications where specific and accurate behavior is critical, like customer service or healthcare.

In developing Eva, Healthier Plate needed a conversational model capable of managing appointment scheduling interactions in a professional yet approachable manner. Eva had to exhibit a consistent tone, maintain clarity, and gather necessary details without prompting user frustration. This case study reveals the hurdles and solutions in programming specific behaviors into a GPT model for a practical scheduling application.

Key Challenges in Programming Specific Behaviors for GPTs

1. Dependence on Training Data

A GPT model learns from its training data, which includes diverse text sources from across the internet. This presents immediate challenges:

• Uncontrolled Data Quality: Since the data isn’t curated specifically for each use case, GPTs can reflect biases, outdated information, or inappropriate tones. For example, a scheduling assistant like Eva, if not carefully guided, might respond too formally or too casually, depending on the user input.
• Behavioral Inconsistencies: The model might switch between tones or lose its contextual alignment. In Eva's case, it was essential to maintain professionalism throughout each interaction, even if the user’s tone shifted.
• Adaptability to New Contexts: Training data can’t anticipate every potential scenario. For example, Eva needed to handle specific questions about Healthier Plate’s services, requiring careful customization to ensure consistent, relevant responses.

2. Prompt Dependency and Behavioral Drift

One common method to guide GPT behavior is through prompt engineering, which involves designing instructions to direct the model’s responses. However, this method introduces unique challenges:

• Prompt Design Complexity: Writing prompts that yield consistent responses requires significant experimentation. Slight adjustments in the prompt can lead to vastly different behaviors, which poses a challenge when aiming for a uniform, reliable conversational style.
• Prompt Length Limits: In extended interactions, models may “drift” from the original prompt, meaning they lose behavioral consistency over time. For Eva, this meant finding a balance where prompts could guide her without causing drift in longer conversations.
• Adapting to Diverse User Behavior: Users ask questions differently, and Eva had to respond appropriately to both direct appointment requests and indirect questions. Ensuring Eva adapted without straying from her professional tone required carefully structured prompts and fallback strategies.

3. Ethical and Safety Constraints

GPT behavior programming must adhere to ethical standards, especially in professional settings like healthcare:

• Avoiding Inappropriate Responses: Despite filtering, GPTs sometimes generate responses that may be offensive or misinterpreted. In Eva's case, it was critical to prevent responses that could misrepresent Healthier Plate’s mission or tone.
• Bias Management: Language models often reflect societal biases present in their training data. Eva needed programming that avoided biases, especially in discussions of health-related topics, to ensure users felt comfortable and respected.
• Legal and Compliance Standards: For a healthcare-adjacent model, it’s essential to avoid responses that might be misinterpreted as medical advice. Eva was designed to provide appointment-related information without overstepping into advisory territory, which could otherwise lead to legal and compliance issues.

4. The Limits of Fine-Tuning and Reinforcement Learning

Fine-tuning and reinforcement learning provide additional methods to guide GPT behavior, but they come with limitations:

• Scope of Fine-Tuning: Fine-tuning adjusts the model to handle specific types of questions, but it doesn’t cover all scenarios. This means that even a well-trained model like Eva may occasionally miss the mark in handling unusual or unexpected inquiries.
• Defining Reward Signals in Reinforcement Learning: Reinforcement learning relies on reward signals that tell the model when it’s performing correctly. For nuanced tasks, such as managing appointment confirmations with empathy, defining these reward signals can be a complex task.
• Balancing Clarity with Conciseness: Eva needed to provide clear, direct answers without sounding verbose. Fine-tuning helped achieve this balance, but perfecting it required continual adjustments based on real-world user feedback.

5. Balancing Flexibility and Control

Striking a balance between allowing flexibility in responses and maintaining consistent behavior is a constant challenge in GPT programming:

• Risk of Overfitting: When models are overly constrained, they may become too rigid, failing to handle novel scenarios creatively. Eva needed some flexibility to address various user needs without overfitting to specific phrases or scenarios.
• Trade-offs Between Generalization and Customization: Creating a generalizable, flexible model that also feels customized and specific is a delicate balance. If Eva’s programming was too broad, it might lose the brand-specific tone needed for Healthier Plate.

Real-World Application: Eva, Healthier Plate's Scheduling Assistant

Eva is a tangible example of how behavioral programming in GPTs can be applied to a business setting. Designed for Healthier Plate, Eva’s primary function is to respond to scheduling inquiries through SMS and email. By providing appointment options, confirming bookings, and answering common questions, Eva enables Healthier Plate to streamline customer interaction while ensuring that users receive accurate and timely responses.

Specific Challenges and Solutions in Developing Eva

Eva’s development involved tackling the issues discussed above and adapting them to Healthier Plate’s specific requirements:

1. Consistency in Tone and Professionalism: Eva was programmed to maintain a warm yet professional tone across conversations, aligning with Healthier Plate’s focus on health and wellness. This required tailored prompts and fallback mechanisms to ensure her tone did not waver, even if the user’s style shifted.
2. Handling Ambiguity in User Requests: Since scheduling questions can vary widely, Eva was designed to clarify information politely and efficiently, without overwhelming the user. Her programming includes follow-up questions to gather missing details when necessary, ensuring that she captures all the information needed for a smooth booking experience.
3. Confirming and Following Up: Once an appointment is set, Eva is programmed to confirm the details concisely and to provide follow-up instructions as needed, ensuring that users feel confident in their booking.

Benefits of Eva for Healthier Plate

Eva’s implementation allows Healthier Plate to maintain high-quality customer service while handling a larger volume of appointment inquiries. She provides consistent answers to frequently asked questions, assists users in real-time, and serves as an extension of Healthier Plate’s commitment to accessible wellness services. As a GPT-based assistant, Eva offers the responsiveness and consistency of an automated tool, with the nuance needed for meaningful interactions.

Conclusion: The Path Forward for GPT Behavior Programming

Eva’s case illustrates both the challenges and successes in programming specific behaviors into GPTs for real-world applications. The difficulties in creating a responsive, consistent, and ethical conversational model are significant, yet the development of Eva highlights that, with careful design, these challenges can be effectively managed. By balancing prompt engineering, fine-tuning, and reinforcement learning, Eva demonstrates that GPTs can become valuable, user-aligned assistants when guided by a thoughtful approach to behavioral programming.

The experience with Eva shows that while the road to creating precisely programmed GPTs is complex, it is feasible. As AI technology advances, we can expect more sophisticated tools to help refine GPT behavior, making these models even more capable of delivering consistent, high-quality service in various sectors. For Healthier Plate, Eva has become an essential tool that not only streamlines appointment scheduling but also reinforces the company’s commitment to user-centric wellness solutions.

Go ahead and give it a try so you can see it in action, this version of Eva is not connected to the backend (it does not schedule appointments), so you can ask anything about Healthier Plate and scheduling.