Evaluating and Debugging Generative AI with Weights & Biases: My Learning Journey

Over the past few weeks, I completed a course on Evaluating and Debugging Generative AI using Weights & Biases (W&B).

As I continue exploring Generative AI, one thing is becoming increasingly clear:

Building GenAI applications is only half the challenge. Evaluating, debugging, and improving them is where real engineering begins.

This course focused heavily on using the features available in wandb.ai to track experiments, visualize results, debug model behavior, evaluate outputs, and improve both diffusion models and LLM workflows.

This article is a reflection of what I learned.

Why Evaluation and Debugging Matter in GenAI

Traditional software is easier to test because outputs are usually deterministic.

Given the same input, we often expect the same output.

But Generative AI is different.

Models can produce outputs that are:

• Creative
• Non-deterministic
• Partially correct
• Visually impressive but semantically wrong
• Fluent but hallucinated

That makes evaluation harder.

For GenAI systems, we need to track more than just whether the code runs. We need to understand:

• What data was used?
• What parameters were changed?
• How did the model behave?
• Which outputs improved?
• Which outputs degraded?
• Can we reproduce the experiment?

This is where tools like Weights & Biases become valuable.

What is Weights & Biases?

Weights & Biases, commonly known as W&B, is an experiment tracking and observability platform for machine learning workflows.

In this course, W&B was used for:

• Experiment tracking
• Model training visualization
• Dataset and artifact versioning
• Evaluation dashboards
• Tracing LLM calls
• Comparing model outputs
• Debugging training behavior

The biggest shift for me was realizing that W&B is not just a dashboard.

It acts as a system of record for machine learning experiments.

1. Instrumenting W&B

The course started with instrumentation.

Before tracking anything, we need to integrate W&B into our training or evaluation workflow.

At a high level, this involves:

import wandb

wandb.init(project="genai-evaluation")
wandb.log({
    "loss": loss,
    "accuracy": accuracy
})

Once instrumented, W&B can capture:

• Metrics
• Hyperparameters
• Training logs
• Model outputs
• Artifacts
• System information

This makes experiments reproducible and easier to compare.

My key takeaway

If you are not tracking your experiments, you are guessing.

Without proper instrumentation, it becomes difficult to know why one run performed better than another.

2. Training a Diffusion Model with W&B

The course then moved into training a diffusion model.

Diffusion models are commonly used for image generation. They learn to generate images by gradually denoising random noise.

During training, W&B helped track:

• Training loss
• Generated image samples
• Hyperparameters
• Checkpoints
• Model artifacts

This was especially useful because image model evaluation is not always obvious from a single metric.

You need to look at both:

• Quantitative signals
• Qualitative outputs

W&B made it easy to visualize generated samples over time and compare how training progressed.

My key takeaway

For generative models, visual inspection is still an important part of evaluation.

Metrics matter, but seeing model outputs helps reveal issues that numbers alone may miss.

3. Evaluating Diffusion Models

Evaluating diffusion models is challenging because generated images may be visually good but still fail in subtle ways.

Some evaluation questions include:

• Are the generated images sharp?
• Are they diverse?
• Are they aligned with the prompt or class?
• Are there artifacts or distortions?
• Is the model memorizing training examples?

W&B supports image logging and side-by-side comparison, which makes this evaluation process much easier.

Instead of manually saving images in folders, you can view generated samples directly in dashboards and compare them across runs.

My key takeaway

Evaluation should combine metrics, samples, and comparisons.

For generative AI, a single metric rarely tells the full story.

4. LLM Evaluation and Tracing with W&B

This was one of the most interesting parts of the course.

LLM applications involve multiple moving parts:

• Prompt templates
• Model calls
• Retrieved context
• Tool calls
• Intermediate reasoning steps
• Final responses

When something goes wrong, debugging can be difficult.

W&B tracing helps capture the full execution flow.

For example, in an LLM workflow, we can inspect:

• Input prompt
• Model response
• Latency
• Token usage
• Intermediate steps
• Evaluation results

This is extremely useful for debugging issues like:

• Hallucinations
• Bad prompt formatting
• Poor retrieval quality
• Unexpected model behavior
• High latency or cost

My key takeaway

LLM debugging requires visibility into the entire workflow, not just the final answer.

Tracing helps convert LLM behavior from a black box into something observable.

5. Fine-Tuning an LLM

The final part of the course covered fine-tuning an LLM.

Fine-tuning is useful when we want the model to become better at a specific task, format, or domain.

Using W&B during fine-tuning helped track:

• Training loss
• Validation loss
• Hyperparameters
• Dataset versions
• Model checkpoints
• Evaluation outputs

This is important because fine-tuning is iterative.

You may need to adjust:

• Dataset quality
• Learning rate
• Batch size
• Number of epochs
• Evaluation criteria

W&B makes it easier to compare runs and understand which changes actually improved the model.

My key takeaway

Fine-tuning without experiment tracking can quickly become messy.

With W&B, every run becomes traceable and comparable.

Why W&B is Useful for GenAI Workflows

After completing the course, I see W&B as useful across the GenAI lifecycle.

1. During experimentation

You can track different model runs, parameters, and outputs.

2. During evaluation

You can compare responses, generated samples, scores, and metrics.

3. During debugging

You can trace what happened inside the workflow.

4. During fine-tuning

You can monitor training progress and compare checkpoints.

5. During collaboration

Teams can share dashboards, runs, artifacts, and findings.

GenAI Evaluation is More Than Accuracy

One of the biggest lessons from this course is that GenAI evaluation is multi-dimensional.

For LLMs, we may care about:

• Correctness
• Helpfulness
• Relevance
• Grounding
• Safety
• Format adherence
• Latency
• Cost

For diffusion models, we may care about:

• Image quality
• Diversity
• Prompt alignment
• Artifacts
• Stability

This means evaluation needs to be designed intentionally.

Before and After Using W&B

Key Learnings

1. Observability is essential for GenAI

As models become more complex, visibility into training and inference becomes critical.

2. Evaluation must be continuous

You cannot evaluate only at the end. Evaluation should happen throughout development.

3. Visual outputs need visual evaluation

For diffusion models, dashboards and sample comparisons are extremely useful.

4. LLM workflows need tracing

Prompts, retrieval steps, tool calls, and final responses should be observable.

5. Fine-tuning needs discipline

Tracking datasets, configs, metrics, and checkpoints makes fine-tuning manageable.

Final Thoughts

Before this course, I looked at GenAI mostly from the perspective of building applications.

Now I see the bigger picture:

Production-grade GenAI is not just about generation. It is about evaluation, debugging, monitoring, and iteration.

Weights & Biases provides a practical way to bring engineering discipline into GenAI workflows.

Whether you are training diffusion models, evaluating LLMs, tracing agent workflows, or fine-tuning models, observability is not optional.

It is part of building reliable AI systems.

Closing Note

If you are working on:

• LLM applications
• Diffusion models
• Fine-tuning workflows
• GenAI evaluation
• AI observability

Weights & Biases is definitely worth exploring.