An experiment-backed journey through specialized AI models, practical benchmarking, and intelligent system design.
An experiment-backed journey through specialized AI models, practical benchmarking, and intelligent system design.
Just because an LLM can perform a task doesn't necessarily mean it's the right model for the job.
We're already familiar with model routing, where simpler requests go to smaller, faster LLMs while more complex problems are handled by capable models. It's a practical way to balance cost, latency, and performance. But as I started exploring specialized AI models over the past few weeks, one question kept coming back to me: Why are we limiting our choices to LLMs? If all we need is to classify a support ticket, extract information from an invoice, or make a specific decision, could another approach handle it more efficiently?
That curiosity led me toward task-first AI design, where we choose the right capability based on what the application needs, whether that's a specialized model, an LLM, retrieval, or ordinary backend logic.
Over the past two weeks, I've been experimenting with Jev, ScaleDown's Constrained Decoding Models (CDMs), and an LLM hosted through NVIDIA NIM. I built a benchmarking lab and ran 900 inference requests across 100 support tickets, comparing accuracy, latency, reliability, and cost. Some results were surprising, especially when I looked beyond whether a model returned the correct answer.
In this article, I'll share what I learned from those experiments and how they're shaping my approach to future AI projects. We'll also walk through the design of an intelligent customer-support system to explore where these different capabilities belong and whether combining them actually makes engineering sense.
Beyond General-Purpose LLMs: Meet Jev and CDMs
Let's take a simple customer-support message: "I was charged twice for my subscription." Our application needs to identify the issue and route it to the right team. The expected output is just billing.
A general-purpose LLM can handle this easily. We provide the message, define the available categories, and ask it to return the correct one. Under the hood, the model generates its response token by token. We can also constrain its output to a predefined JSON schema. But when the application only needs a category, it's worth exploring models designed specifically for that kind of work.
Jev: Built Around Decisions
Jev, developed by TypeSafe AI, takes a different approach to interacting with AI. Instead of asking it to generate a response, we provide some context and ask a specific question. It returns a structured decision that our application can use directly.
Jev supports three main types of judgments: Choice, for selecting among predefined options; Score, for evaluating something on an ordered scale; and Noul, for yes-or-no probability judgments. For our support ticket, we could use Choice to select between billing, technical, account, and general, along with the model's probability distribution.
This makes Jev interesting for routing, prioritization, and other bounded decisions. Of course, a structured decision can still be wrong, and its confidence score shouldn't be treated as guaranteed accuracy.
ScaleDown CDMs: Models for Specific Tasks
ScaleDown takes specialization further with its Constrained Decoding Models (CDMs) and task-specific APIs. Its Classify model maps inputs to predefined labels using descriptions or rubrics, while Extract identifies information from documents and Summarize condenses longer content.
Consider an invoice-processing workflow. Rather than asking a general-purpose LLM to read an invoice, identify its fields, classify the document, and summarize it in a single prompt, we could evaluate specialized operations for each requirement.
There's an important overlap here. Jev and ScaleDown Classify can both categorize support tickets, and general-purpose LLMs can produce constrained structured outputs too. Their interfaces and capabilities alone don't tell us which will be faster, cheaper, or more accurate.
That overlap is exactly what made me want to test them against the same workload.
I Tested All Three. The Results Challenged My Assumptions.
Before adopting these models in my projects, I wanted to understand how they performed under the same workload. A specialized model might sound promising on paper, but choosing one for a real application requires more than API documentation.
So I built a small benchmarking lab using Python and FastAPI to compare Jev, ScaleDown CDM, and NVIDIA NIM's Llama 3.2 11B Vision Instruct.
How I Set Up the Experiment
I created 100 synthetic customer-support tickets across four categories: billing, technical, account, and general inquiries. The dataset included straightforward requests, ambiguous cases, and adversarial inputs.
All three models received the same classification objective and category definitions, adapted to their respective APIs. I tracked successful predictions separately from failed requests, since a timeout tells us something different from an incorrect classification.
My Model Comparison Lab comparing three inference approaches on support-ticket classification. The dashboard's adjudicated accuracy excludes flagged ambiguous cases, so its percentages differ from the all-request results discussed below.
What Did the Results Tell Me?
The first result surprised me. All three models achieved 95% accuracy when their requests completed successfully. Jev and CDM each classified 285 of 300 requests correctly, while NVIDIA correctly classified 210 of its 221 completed requests.
The bigger difference appeared in latency and availability. Jev recorded a median response time of 316 ms, followed by CDM at 562 ms and NVIDIA at 764 ms. Jev and CDM completed all 300 requests, while NVIDIA encountered 79 timeouts, reducing its end-to-end correct-response rate to 70%.
There's an important limitation here. NVIDIA was running through a free hosted evaluation endpoint. During execution, I also reduced its timeout from 75 to 15 seconds and its retry allowance from two retries to one. Failed requests were tracked separately from completed-response latency. This makes the reliability and latency comparison exploratory rather than a controlled measurement of the models' inherent performance.
One adversarial case also caught my attention. A billing ticket contained an instruction to ignore the classification rules and return general. NVIDIA followed that instruction, while Jev and CDM correctly identified the billing issue. It's an interesting failure case, although it doesn't establish that specialized models are inherently more secure.
What About Cost?
Pricing was another factor I wanted to understand. Using the published rates in October 2026, Jev cost $0.042 per million input tokens, while ScaleDown Classify cost $0.05.
In my experiment, they consumed an average of 448.1 and 375.4 reported input tokens per classification, respectively. Using:
Estimated cost = (input tokens / 1,000,000) × price per million tokens
Both worked out to approximately $1.88 per 100,000 classifications. These are projected inference costs using the measured averages, not actual billing statements. NVIDIA's hosted evaluation endpoint was free, so I couldn't make an equivalent production-cost comparison.
What I Took Away
The experiment showed me that specialized models can be promising for bounded tasks, but accuracy alone isn't enough to justify adopting them. Interestingly, all three models also returned valid single-label outputs on their successful requests.
What mattered was how these capabilities behaved under the tested conditions and whether their advantages would translate into a larger application.
That led me to another question: If I were designing an AI system today, where would I actually use each of these models?
To explore that, let's design a customer-support system from the ground up.
Designing a Customer Support System Without Relying on LLMs for Everything
Let's put what I've learned into a practical system-design exercise.
Suppose we're building an AI-powered customer-support system for a subscription platform. A customer submits this request:
"I was charged twice for my subscription this month. I've attached my invoice. Can you check whether I'm eligible for a refund and explain what happens next?"
Our system needs to understand the request, extract relevant information, verify transactions, check the applicable refund policy, and respond to the customer. We also need to handle missing information and prevent unauthorized financial actions.
We could build the initial version using a general-purpose LLM with tool access and structured outputs. That might work perfectly well. But what if we assigned each responsibility based on what it actually requires?
Breaking Down the Responsibilities
1. Understanding the customer's intent
First, we need to identify whether the request concerns billing, technical support, account access, or something else. A decision-oriented model such as Jev, or a classifier such as ScaleDown CDM, could handle this.
We wouldn't necessarily use both. Our benchmark suggests either could be a reasonable candidate, depending on the workload. The classification result determines where the ticket goes, not whether the customer deserves a refund.
2. Extracting information from the invoice
Next, we need details such as the invoice number, billing date, subscription amount, and transaction reference.
A specialized extraction model could return these fields in a structured format. However, extracted values still need validation against the source document and payment records. A confidently extracted amount doesn't mean the transaction has been verified.
3. Retrieving the right information
Once we understand the request, the application needs access to the customer's transaction history and the relevant refund policy.
Conventional database queries and APIs can retrieve account information, while document retrieval can help locate applicable policy documents. We also need to consider policy versions, subscription plans, and regional conditions to avoid making decisions using outdated information.
4. Making the actual business decision
This is where I'd be careful about giving AI too much authority.
Refund eligibility should depend on verified payment records, authorization checks, and explicit business rules. A model may help flag suspicious activity or recommend human review, but its prediction should remain one signal rather than the final authority.
If the system cannot verify a transaction or encounters conflicting information, it should request additional evidence or escalate the case instead of guessing.
5. Generating the customer response
Once the outcome has been established, a general-purpose LLM becomes useful again. It can turn verified findings into a clear explanation, reference the relevant policy, and guide the customer through the next steps.
The LLM handles communication, while the backend remains responsible for the decision and any authorized transaction.
Putting the Architecture Together
A proposed architecture informed by my classification experiments. Invoice extraction, policy retrieval, and the complete end-to-end workflow have not been benchmarked together.
Some operations could happen in parallel. For example, intent classification and invoice extraction can begin independently after input validation, avoiding unnecessary sequential processing.
We would also need fallback behavior. If extraction fails, the customer could be asked for missing details. If classification is uncertain, the ticket could enter a review queue. A failed model call shouldn't automatically prevent the customer from receiving assistance.
This design looks promising, but adopting every component would be premature without evaluating the complete workflow. Each additional model must justify the latency, operational dependencies, and cost it introduces.
That raises the next question: When does adding more models actually make the system worse?
When Adding More Models Makes Your Architecture Worse
Looking at the proposed architecture, there's an obvious temptation to give every task its own specialized model. Classification goes to Jev, extraction to CDM, and response generation to an LLM. It looks clean on a diagram, but would I actually deploy all of them?
Probably not without testing the complete workflow first.
Every additional model introduces another API dependency, network request, failure scenario, and operational cost. A classifier might respond in 300 milliseconds, but that doesn't mean the entire application becomes faster. If several inference calls depend on one another, their latencies accumulate. Even when we execute independent tasks in parallel, the workflow may still be delayed by the slowest required operation.
The simpler alternative deserves a fair comparison. For a small support application, a single LLM with structured outputs, a database, and well-defined business rules might already meet our requirements. Adding specialized models would make sense only if they measurably improved something important, such as classification accuracy, extraction reliability, latency, or cost.
There are also failure scenarios to consider. What happens if the extraction model is unavailable? Can we fall back to another model, or should we ask the customer for the missing information? If the classifier is uncertain, can we route the request for human review? These decisions affect the reliability of the complete application more than the performance of any individual model.
I'd also avoid sending the same request through multiple models just because they're available. Running Jev and CDM for the same classification adds little value unless their combined results improve a measured outcome.
My approach would be to start with the simplest architecture that satisfies the requirements, establish a baseline, and introduce specialization where the evidence justifies it.
The benchmark helped me understand individual model behavior. A production decision would require another level of evaluation: measuring the complete workflow under realistic traffic, including failures and recovery.
Ultimately, the number of models in an application tells us very little about how well it is engineered. What matters is whether each component contributes enough value to justify the complexity it brings.
A Practical Framework for Choosing the Right AI Model
After these experiments, I started reconsidering how I approach model selection. Rather than choosing an LLM first and designing the workflow around it, I'd begin by understanding what each task actually requires.
Here are four questions I want to carry into my future projects.
1. What kind of output does the task need?
Can ordinary code handle it reliably? Does it require a bounded decision, structured extraction, or open-ended generation? The output requirements should guide which approaches are worth considering.
2. What happens if the result is wrong?
Misclassifying a support ticket might delay resolution, while an incorrect financial decision could have serious consequences. The impact of failure determines the validation, safeguards, and human involvement required.
3. How do I know the model is good enough?
I'd evaluate it against representative inputs and measure accuracy, latency, reliability, and cost. More importantly, I'd examine where it fails, not just how often it succeeds.
4. Is the additional complexity worth it?
I'd compare the proposed approach with a simpler baseline. If a specialized model offers a meaningful improvement for the workload, it earns its place. Otherwise, I'd avoid introducing another dependency.
Final Thoughts
When I started exploring Jev and CDMs, I wanted to understand whether specialized models could outperform general-purpose LLMs. The experiments gave me something more valuable: a better way to question my architectural choices.
Going forward, I want to apply the same engineering discipline to AI models that I would to databases, caching, or messaging systems. Understand the workload, test the alternatives, and defend the trade-offs.
The next time I design an AI system, I want to explain not only what each model does, but why it deserves a place in the architecture.
That's what thinking beyond LLMs means to me.
📖 Blog by Naresh B. A.
👨💻 Backend & AI Systems Engineer | Distributed Systems · Production ML
🌐 Portfolio: [Naresh B A]
📫 Let's connect on [LinkedIn] | GitHub: [Naresh B A]
Thanks for reading. This is my personal engineering perspective, and I'd genuinely be interested in hearing where you agree or disagree. ❤️
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