Building ChatGPT / LLM Wrapper Products Is Not Trivial (Part 1)

Building production-grade ChatGPT / LLM (Gen AI) wrapper products has it's own unique set of challenges. A deep dive into the "considerations" to keep in mind when building Gen AI products. 🪄

In tech circles, the phrase "ChatGPT wrapper" is uttered with disdain, as if it’s shorthand for triviality. But anyone who has worked on building scalable, production-grade Gen AI products knows the truth: creating a "wrapper" that solves real-world problems is not as simple as it seems.

Whether you’re an entrepreneur, product manager, developer, AI enthusiast, or simply a stranger lurking on the Internet, you will find a gold-mine of information in here. So grab a cup of coffee (or your favorite beverage) and dive in!

Here’s why building such products demands as much engineering depth, business acumen, and creativity as any technological innovation.

Business Domain Modelling ⩤

At the heart of every successful software product, whether powered by AI or not, is its ability to solve real business problems. This begins with translating complex, often ambiguous business requirements into structured, actionable specifications. This is Software Development 101, now enter AI.

For AI systems, particularly ChatGPT wrappers, this challenge escalates because you’re not just designing software; you’re shaping how an intelligent system understands, interacts with, and adapts to a specific domain.

Consider the development of a legal assistant powered by ChatGPT. According to a report by Harvard Law School and other legal research providers, there are approximately 100 million case law documents available in the U.S. legal system, with approximately 3,000 new cases being added weekly. To make this assistant effective, one needs to define domain entities such as contracts, clauses, and precedents, as well as their intricate relationships and workflows. For instance, determining how a "force majeure clause" interacts with a "breach of agreement" requires precise domain modelling to prompt an Large Language Model (LLM). Given the stakes in legal contexts, even a minor misinterpretation can result in significant liabilities or erroneous advice.

These intricacies must then be embedded into the AI through structured prompts and user interfaces. A poorly modeled prompt could lead to the assistant failing to distinguish between binding and non-binding precedents—a potentially catastrophic failure in a legal context. A report from Gartner (AI and the Future of Work: 2030 and Beyond, Source: Perplexity) states that approximately 70% of AI projects fail due to issues related to poorly defined requirements and a lack of communication between stakeholders. For a project such as this, bridging the gap between complex legal nuances (business domain knowledge) and effective prompt design (system design) is paramount.

According to a study by McKinsey, about ~60% of organizations are adopting AI in atleast one business area. This makes the development of production-grade AI systems even more valuable for organizations and the path to developing them hinges on our ability to translate complex & ambiguous domain principles into clear, actionable specifications.

Integration with Third-Party Services 🔌

Modern software rarely operates in isolation, and LLM-powered systems are no exception. To maximize the value of a ChatGPT wrapper, integrating with third-party services, both external like Google Maps, Stripe, or Twilio, and internal such as enterprise databases or proprietary APIs is often essential. For instance, a sales assistant app may require integration with a CRM like Salesforce, email services, and calendar APIs while interacting with the LLM (most likely via tool calls or custom agentic workflows). According to a survey by Gartner (Source: Perplexity), about 68% of organizations report that integrating third-party services is becoming increasingly complex, highlighting the need for effective strategies in this area.

Handling these integrations involves navigating challenges such as authentication, rate limits, and potential service failures. The AI must work gracefully with the integrated services to provide coherent responses, but external data can introduce unpredictability during inference. API integrations face issues related to response time and reliability, which can negatively impacts the user experience. Architecting a reliable system requires not just technical skills but also a strong focus on product management to keep everything aligned with the long-term objectives of the business.

The complexity of integrating diverse services necessitates the creation of a clean, unified abstraction layer. This layer should shield the language model from external inconsistencies while providing a stable interface for users. Companies that prioritize such architectural resilience can improve their system performance. The success of an AI-powered system often depends upon how seamlessly it interconnects and interoperates with various services while maintaining high-quality user experience.

Human-Centric Design 💁🏻‍♂️

AI products fundamentally differ from traditional software in their user interactions. A ChatGPT wrapper serves not just as a transactional tool but as a partner to humans, making factors like clarity, empathy, and accessibility crucial in design. For example, consider a visually impaired user engaging with an AI providing customer support. According to the World Health Organization, about 2.2 billion people globally have some form of vision impairment, underscoring the importance of designing inclusive experiences that accommodate all users.

Accessibility goes beyond visually impaired users; it also involves ensuring the AI communicates clearly without overwhelming the user in a live chat setting. The challenge lies in translating natural language queries into effective prompts for the LLM. Research shows that up to 70% of users (Source: Perplexity, Microsoft’s Research on AI and User Frustration, Pew Research on User Perceptions of AI) report frustration when their queries are misunderstood or inadequately addressed. Poor experiences at this stage lead to ineffective outputs, eroding user trust and satisfaction in the system.

Ultimately, designing an effective ChatGPT / LLM wrapper transcends technical engineering; it focuses on creating intuitive and delightful interactions for all users. Companies that prioritize human-centric design strategies see user engagement increase the overall user satisfaction. By focusing on empathy, clarity, and accessibility, we can build Gen AI products that resonate deeply with users, ensuring that their experiences are both meaningful and empowering.

Vendor Lock-In 🔐

Building on an LLM means you are inherently tied to the vendor's API, pricing, limitations, and future roadmap. This can be a double-edged sword. While you gain access to advanced AI capabilities without the burden of developing the underlying model, you also face critical risks related to dependency. For example, if OpenAI were to change its pricing structure or impose stricter limits on API usage during peak business hours, it could severely disrupt your service.

A survey by Gartner (Source: Perplexity) indicates that around 50% of companies report facing serious issues due to unexpected vendor changes. Mitigating these risks demands a resilient architecture, incorporating fallback systems and possibly exploring multi-vendor support. This strategy allows for greater flexibility and reduces the likelihood of vendor lock-in. However, designing such a system is complex and not without challenges. According to industry reports, approximately 40% of organizations struggle with integrating multiple vendors effectively, which can lead to increased costs and resource allocation.

Organizations that prioritize building adaptable systems can improve their operational resilience by enabling them to respond swiftly to changes in vendor policies. By adaptable, I mean designing systems where the LLM isn’t a vendor bottleneck, you can swap-in and swap-out a vendor as per the user requirements and required design / architectural trade-offs. This would include model routing, supporting other LLM providers like Anthropic or Google Gemini or using Open Source solutions like Mistral. Ultimately, the goal is to create a product that minimizes dependency risks while optimizing long-term maintainability.

Architectural & Infrastructure Limitations 🗺️

Building a generative AI product goes far beyond writing clever code; it requires a robust infrastructure capable of supporting production-scale demands. A well-designed ChatGPT wrapper must handle real-time interactions, which introduces a host of challenges related to scaling. Factors such as latency, concurrency, and cost-effectiveness are crucial and directly impact the bottom line. Moreover, an LLM module or service won’t exist in isolation (as I previously explained), it will work in tandem with other business-critical services.

The costs associated with maintaining compute resources for logging, monitoring, and analytics can add up quickly. Implementing solutions like Grafana, Prometheus, or Datadog is essential for performance monitoring and observability, but they come with their own expenses. For instance, while Grafana is open-source, utilizing advanced features or Grafana Cloud can incur significant costs. Similarly, Datadog operates on a subscription model based on the number of hosts being monitored, which can escalate with high-volume traffic. This financial burden must be weighed against the need for real-time insights and operational efficiency.

Programming complexities also play a critical role in the successful orchestration of a generative AI product. Issues surrounding concurrency become particularly challenging when multiple users access the system simultaneously.

The self-deployment of generative AI models brings its own set of complexities. Organizations may choose to deploy models on-premises or in a hybrid cloud setting to maintain control over their data and reduce ongoing costs. However, this requires significant expertise in infrastructure management, including setting up container orchestration tools like Kubernetes. The deployment process must also include considerations for scaling up during high demand and rolling out updates without downtime, which can be technically daunting.

When integrating LLMs into a microservices architecture, additional complexities arise. Each microservice must not only communicate efficiently with the LLM but also with other services. This inter-service communication introduces potential bottlenecks and increases the chance of failure, particularly when network latency or service dependencies are involved. If there are events, then it there are further challenges, which is out of scope for this article.

From the costs associated with monitoring tools to the intricacies of programming concurrency and rate limits, each element must be addressed meticulously.

Evaluation 🧪

Evaluating traditional software systems is relatively straightforward. Functional requirements either pass or fail. In contrast, assessing LLM-powered systems resembles the evaluation of poetry; it is subjective, contextual, and unpredictable. For instance, defining "success" for an AI-generated response can vary widely. Are you looking for accuracy, creativity, or alignment with a specific brand tone? This ambiguity complicates the evaluation process and necessitates more nuanced metrics than conventional test cases.

To effectively assess AI responses, evaluation frameworks must include robust metrics such as task success rates, user satisfaction scores, and consistency across varying inputs. According to research, over 65% of AI implementations face challenges in measuring and validating performance, highlighting the need for well-defined evaluation standards. Without these metrics, it becomes difficult to determine how well the AI is performing or how to improve it, making the job of building a high-quality wrapper does require engineering a sophisticated evaluation system than just providing a simple interface.

A well-designed evaluation framework is essential for understanding the effectiveness and reliability of LLM responses. Organizations that prioritize comprehensive evaluation strategies can increase their AI performance ratings by up to 50%, ensuring that the outputs align with user needs and expectations. By focusing on these complex evaluation metrics, we can create systems that not only generate responses but also resonate meaningfully with users, enhancing overall interaction quality.

Testing and Predictability ⚙️

Testing software is inherently challenging, but creating a wrapper around a large language model (LLM) requires taking care of a few things. The non-deterministic nature of these models means that the same input can yield different outputs at different times. Traditional unit testing methods simply do not suffice in this scenario. You will most likely end up mocking AI generate methods for your unit tests. For example, in a customer support chatbot, a user might ask the same question and receive varying responses regarding policy details, which can confuse and frustrate users.

To ensure reliability, a comprehensive testing strategy must combine regression testing, exploratory testing, and user acceptance testing. Regression testing helps catch unintended changes in behavior due to updates, while exploratory testing allows for the investigation of edge cases that may not be covered by predefined scenarios. User acceptance testing verifies that the AI performs well in real-world situations. According to industry data, over 60% of companies (source: McKinsey, Gartner, Perplexity) report that the unpredictability of AI outputs significantly increases their testing costs and timelines.

Ultimately, the complexity of testing LLM wrappers underscores the need for a sophisticated and adaptable approach to quality assurance. Organizations that invest in robust testing frameworks can improve their AI reliability metrics by ensuring that users receive consistent and meaningful interactions. By prioritizing rigorous testing processes, we can better manage the inherent unpredictability of LLMs and deliver high-quality products that meet user expectations.

Prompt Engineering 📝

One of the most underestimated aspects of building LLM products is prompt engineering. Crafting prompts for the model goes far beyond simply writing "requests"; it involves designing dynamic conversations that balance clarity, brevity, and specificity while aligning with business objectives. For example, an AI customer support system must generate responses that are not only accurate but also friendly and helpful, often requiring different prompts for various customer intents.

Prompts become first-class citizens in the system (almost). I say “almost” because prompts don’t work in an isolated manner (like most software components), they require application-level handles to be useful. Prompts need to be maintained, evaluated, tested, and updated as the product evolves. While it’s easy to fall into the trap of thinking that “why waste so much time on a simple string that I can easily change?”, remember this:

• Prompts are the singular, most-definitive inputs to an LLM.

• The LLM output depends on the prompt, so keeping a track of different prompts during evaluation provides insight into the LLM’s behaviour.

• Prompts need to be tested because incorrect prompts can lead to poor quality responses which has direct user impact.

• Prompts are written in natural-language, so all the rules of language and human understanding apply. Two prompts which “sound” or “look” similar could have completely different results in practice.

Prompt engineering is an iterative process of experimentation and constant tuning. Subtle changes in phrasing or formatting can lead to drastically different outcomes. Research shows that up to 80% of AI performance (Source: Perplexity) can be influenced by the quality of prompts, highlighting the critical nature of this task. Additionally, prompts must be adaptable, customized according to user inputs, and localized to fit the cultural context of different regions or industries. Achieving this level of sophistication at scale requires robust systems capable of generating, testing, and adapting prompts in real time.

Ultimately, effective prompt engineering is the linchpin that ensures successful interactions between users and the AI.

The Verdict: It’s More Than Meets the Eye 🌤️

A ChatGPT wrapper might sound like a simple façade over a pre-existing technology, but the reality is far from it. After we understand the challenges of building Gen AI products, we learn that it demands as much rigor, expertise, and innovation as any other software product.

So, the next time someone dismisses LLM-based apps as "just a wrapper," remember this: simplicity in user-facing design often hides immense complexity under the hood. And engineering that simplicity, while delivering value at scale, is a challenge only seasoned builders can appreciate.

So go ahead, build that LLM wrapper! It will be a fun journey.

Watch out for Part 2! ✨

This is a vast topic. I tried to cover the most important considerations, and had to skip a few details to keep the article readable in one sitting. I will include further depth, detail and more considerations in the sequel to this post (Part 2). This would cover a slightly broader scope, which would include “startup” specific info.

In Part 2, I will cover:

• Application Layer Complexities

• Customization & Fine Tuning

• Gen AI Budgets & Pricing

• Data & Knowledge Governance

• Security & Regulatory Challenges

• Multi-Modality

• The Price of “Easy Replicability” (..or, competitors copying your product)

• Strategic Differentiation

• Scaling Challenges

• Responsible AI & AI Safety

Stay tuned! 🚀

— Aditya Patange (AdiPat)

If you're looking for someone to build your startup MVP, contact me!

I actively work on Open Source Software, check out my GitHub Profile. ✨

Follow me on Instagram (@adityapatange), I talk about tech, meditation, startups and hip hop! ⚡️

I write byte-sized insights on Threads to supercharge your day. 💡