From Logs to Intelligent Choices - Inside Noveum.ai’s Evaluation Process

Introduction

Every month, a new AI model hits the spotlight. Whether it’s a GPT variant, Claude’s latest tweak, or something entirely new like DeepSeek, keeping up can feel like chasing a moving target. Which model truly meets your specific needs, and how do you compare them all?

That’s exactly the challenge Noveum.ai solves. Rather than guess—or worse, rely on model hype—Noveum.ai lets you record your real-world AI usage, transform those logs into a dataset, and run evaluation (“eval”) jobs to see which model performs best. If you’ve ever wondered how this all comes together, you’re in the right place.

Step 1: Logging Your AI Calls

The Magic Gateway

At the heart of Noveum.ai is the AI Gateway—a lightweight piece of technology you install with just a single-line code change. Instead of calling OpenAI, Anthropic, or DeepSeek directly, your application sends the request to the gateway.

This gateway:

Captures crucial data (prompt, response, latency, cost, etc.).
Routes the request to the right provider.
Stores logs securely in your chosen environment—your own cluster or our hosted option.

Why does this matter? Because these logs form the foundation for everything that follows. They’re the real-world evidence of how users interact with your AI and how your models behave day-to-day.

Privacy & Control

We get it: data is sensitive. That’s why we give you options:

Encryption at rest and in transit if you use the Noveum hosted solution.
Self-hosted data pipelines if you want to keep logs 100% in your own environment.

Either way, the gateway ensures you don’t have to build a custom logging solution from scratch.

Step 2: Creating a Dataset

From Raw Logs to Actionable Data

Once the gateway is in place, you’ll accumulate a rich library of logs containing every prompt, model response, cost metric, and error message. The next step is to turn that log data into a curated dataset.

What is a Dataset?

A dataset is a collection of real prompts and correct (or desired) responses. Think of it as your “golden set” of examples that captures the important user requests (and the best outcomes you want in return).

Building Your Dataset

Filter & Review: Not every request is ideal for evaluation—some might be test queries or incomplete. Use Noveum’s interface to filter and select interactions that matter most.
Label Outcomes: Mark what’s correct, what’s incorrect, and what’s “good enough.” You’re effectively grading your AI’s performance right from the logs.
Enhance with Metadata: Add context, user demographics, or any other details that matter for accurate evaluation. This ensures your dataset reflects real-world scenarios, not contrived ones.

Step 3: Running Eval Jobs

What Are Eval Jobs?

An eval job is where the magic happens: Noveum.ai systematically tests one or more AI models against your curated dataset. Instead of you manually writing code to feed each example to GPT-4, Claude, or DeepSeek, Noveum does it automatically—logging the outcomes in a standardized format.

The Eval Flow

Select Models: Pick which model(s) you want to compare. It could be GPT-4 vs. Claude, or any new contender that’s popped up on Hugging Face.
Send Dataset: Noveum sends your curated dataset prompts to each model.
Capture Responses: We store how each model replies, plus the usual stats (latency, tokens, cost, etc.).
Evaluator Modules: A panel of large language models (or other evaluators) compares the new response to your dataset’s “ideal” answers. This can be a direct match or a more nuanced approach—sometimes we let an LLM judge the correctness.
Aggregate Results: We roll everything up into an easy-to-read report, ranking each model by accuracy, speed, and cost efficiency.

Evaluators in Action

Our “panel of LLMs” approach can be surprisingly robust. For instance, if you’re testing math questions, the evaluator can parse numeric answers or reason about step-by-step solutions. For legal or creative tasks, it checks if the solution meets specific style or compliance guidelines.

Why does this matter? Because not all tasks are strictly factual. Some are about style, clarity, or even brand voice. Our flexible evaluation system accounts for that.

Why This Matters

Data-Driven Decisions
Instead of picking a model because it’s “trending,” you choose based on real logs and actual performance on your tasks.
Time Efficiency
Manual experiments can take days or weeks. Eval jobs make it a few clicks, freeing your engineers for higher-level work.
Cost Transparency
Ever been shocked by your AI bill at the end of the month? With Noveum.ai, you see cost data in every log and can optimize accordingly.
Dynamic Updates
Your logs grow daily; so does your dataset potential. Each new wave of interactions can be curated and tested against fresh models.

A Closer Look at Datasets, Evaluators, and Eval Jobs

Datasets

Purpose: Represent the real-world tasks your AI needs to handle.
Content: Inputs (questions, prompts) paired with “correct” or “ideal” answers.
Creation: Culled from logs, labeled by your team.

Evaluators

Purpose: Score each response against your ideal answers or criteria.
Method: We can use specialized scripts, direct reference checks, or a panel of LLMs to judge subjective or complex answers.
Flexibility: Evaluate everything from short Q&As to long-form creative tasks or domain-specific queries.

Eval Jobs

Purpose: The orchestrated process that compares multiple models on your dataset.
Scope: You define which models to test, what your priorities are (accuracy, speed, cost), and how the evaluation is run.
Outcome: A transparent ranking, plus detailed performance metrics that inform your model choices.

Real-World Example

Let’s say you run a customer support platform. You’ve built a nice dataset of 1,000 real user queries with validated support answers. You think GPT-4 works well, but you’re curious about Claude 2 or a brand-new open-source model.

Logs to Dataset: Over a week, your gateway logs calls made by actual customers. You tag the correct answers and flag the user queries that matter most.
Eval Job Setup: In Noveum’s dashboard, you select GPT-4, Claude 2, and your open-source model to compare.
Execution: Noveum.ai runs all 1,000 queries through each model, collecting responses.
Scoring: A panel of LLMs checks if each response aligns with your curated “correct” solutions.
Results: You discover GPT-4 is 95% accurate but pricey, the open-source model is 80% accurate but saves you 60% in cost, and Claude 2 sits in between. You can now make an informed decision based on actual numbers.

Future-Proofing Your AI Stack

New models are released constantly—some large, some fine-tuned for niche tasks. With Noveum.ai, you don’t have to scramble to integrate and manually test them. Just add the model to your gateway’s provider list, run an eval job, and let the data tell you if it’s a winner.

Looking Ahead

We’re also expanding into other modalities like image, video, and audio. The process remains similar: gather logs, build datasets, run eval jobs. That consistent approach is key—no matter what medium your AI application handles, you can compare and optimize at scale.

Conclusion

The power of Noveum.ai rests in a seamless loop:

Collect real-world usage logs.
Curate a dataset reflecting your actual user needs.
Launch eval jobs to see which model really shines.

Every new request that hits your application becomes part of a bigger, more detailed picture of how your AI performs. Instead of guessing, you’re acting on concrete data. And with data-backed decisions, you can confidently scale up, switch models, or even fine-tune your own in-house LLM, knowing you have the logs and evaluations to guide you.

Ready to see it in action? Check out our private beta or reach out to our team if you have any questions. We’re here to help make your AI stack smarter, cheaper, and more impactful—one log, one dataset, one eval job at a time.