Measuring Brilliance in Generative AI: Perplexity, Precision, and Faithfulness

We unpack how to evaluate AI that writes and creates, not just predicts. Why perplexity captures surprise, why a low perplexity score isn’t a guarantee of correctness, and how precision, recall, and the harmonic F1 balance model performance. We compare BLEU and ROUGE, explore Retrieval-Augmented Generation to stay faithful to private data, and discuss out-of-domain challenges, agentic AI, and the guardrails shaping the future.

Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

Sponsored by Embersilk LLC

Transcript

SPEAKER_00 0:00

So the other night, um, I asked an AI to summarize a quick lasagna recipe for me, right?

SPEAKER_01 0:06

Okay. How did that go?

SPEAKER_00 0:07

Well, it confidently told me to add uh three tablespoons of crunchy blue gravel.

SPEAKER_01 0:12

Oh no. Blue gravel.

SPEAKER_00 0:14

Right. And I mean it is funny, but it really highlights a massive challenge. We are building these absolutely incredible, optimistic tools for the future. Humanity is literally on the brink of solving our biggest problems with this tech.

SPEAKER_01 0:27

Exactly. It is such an exciting time.

SPEAKER_00 0:29

It really is. But um how do we actually measure if an AI is brilliant or just you know, confidently hallucinating? Because traditional machine learning scoring is relatively binary, right?

SPEAKER_01 0:40

Aaron Powell Yeah. With traditional models, you have clear right and wrong answers. But evaluating generative AI is just a completely different beast. I mean, concepts like fluency and creativity, they're inherently subjective.

SPEAKER_00 0:52

Right. You can't just use standard accuracy metrics when the output is a recipe or like a poem. You need rubrics that actually capture the nuance of language. So before we grade these systems, we kind of need to remember how they learn. Like if supervised learning is basically using flashcards with labeled answers, and uh reinforcement learning is like training a puppy with treats, where does generative AI fit in?

SPEAKER_01 1:16

Aaron Powell Well, it builds on all of that to actually create new content. But because it is generating, we need completely different tests.

SPEAKER_00 1:23

Okay. So if my AI gives me gravel in my lasagna, what mathematical tests did it actually fail?

SPEAKER_01 1:30

It likely failed on perplexity.

SPEAKER_00 1:32

Perplexity.

SPEAKER_01 1:32

Yeah. Perplexity literally measures how surprised an AI model is by the next word in a sequence. So if it expects to output the word ricotta, but the uh data pathways force it to output gravel, its mathematical surprise just spikes.

SPEAKER_00 1:47

Oh wow. So a lower perplexity score means the model is highly confident and fluent?

SPEAKER_01 1:52

Precisely. It is calculating the probability distribution of the next token and staying within expected bounds.

SPEAKER_00 1:57

But fluency doesn't actually mean it is right. Like it was very fluently telling me to eat rocks.

SPEAKER_01 2:02

Yeah, that is the catch.

SPEAKER_00 2:03

So how do we measure the actual correctness?

SPEAKER_01 2:06

Well, that brings us to the tension between precision and recall. Precision minimizes false positives. It is making sure what you retrieve is strictly correct.

SPEAKER_00 2:18

Okay, so precision is like using a sniper rifle.

SPEAKER_01 2:20

Great analogy, yes. Yeah. And recall minimizes false negatives. It makes sure you catch every possible correct instance.

SPEAKER_00 2:27

Like casting a massive fishing net.

SPEAKER_01 2:29

Exactly.

SPEAKER_00 2:29

I hear developers obsess over getting a perfect F1 score to balance these out, but isn't that flawed? If I am building an AI for medical diagnosis, I care way more about recall, right? Catching every sick patient, even if I get a few false positives.

SPEAKER_01 2:44

That makes total sense.

SPEAKER_00 2:44

So why treat the F1 score as the holy grail if it just averages them out?

SPEAKER_01 2:48

Because it is not a simple average. The F1 score is a harmonic mean.

SPEAKER_00 2:53

Oh, a harmonic mean.

SPEAKER_01 2:54

A simple average would let a terrible precision score hide behind a perfect recall score. But a harmonic mean aggressively penalizes extreme imbalances.

SPEAKER_00 3:04

I see. It forces the model to perform well on both fronts.

SPEAKER_01 3:07

Exactly. But you're totally right to push back. If your use case demands high recall, you absolutely adjust your metrics. For translation, we use metrics like BLEU to heavily weight precision.

SPEAKER_00 3:20

Right. Whereas summarizing text probably relies on OG to prioritize recall.

SPEAKER_01 3:24

You got it.

SPEAKER_00 3:24

Okay, so the math forces exactness and coverage. But um, what if a perfectly tuned model simply doesn't know your specific company data?

SPEAKER_01 3:33

Right, the out-of-domain problem.

SPEAKER_00 3:35

Yeah, because we obviously don't want to retrain a massive foundation model from scratch just for HR policies.

SPEAKER_01 3:41

And you really shouldn't. That is where RAG, retrieval augmented generation, is so powerful. Instead of forcing the AI to memorize the entire internet, which is what techniques like distillation try to compress into smaller student models, our RAG just gives the AI an open book test.

SPEAKER_00 3:56

That is so much more efficient.

SPEAKER_01 3:58

It really is. It can go into your private database, pull the exact document, and quote it directly. That is why we measure faithfulness in RAG. Did it actually read the document or did it just guess?

SPEAKER_00 4:07

But keeping a model strictly faithful to its context is um incredibly difficult to do on your own. You usually need an integration partner, which actually brings us to today's sponsor.

SPEAKER_01 4:18

Oh, perfect timing.

SPEAKER_00 4:19

Right. This deep dive is sponsored by Embersilk. If you need help with AI training, automation, integration, or software development, or if you are uncovering where agents could make the most impact for your business or personal life, check out Embersilk.com for AI needs.

SPEAKER_01 4:34

Setting up those guardrails is crucial because we are rapidly moving toward agentic AI.

SPEAKER_00 4:39

Agentic AI. Yeah.

SPEAKER_01 4:41

AI systems that don't just answer questions, but autonomously break tasks down, plan actions, and use tools to execute complex workflows. Combined with human in-the-loop practices, the potential to solve massive global challenges is phenomenal.

SPEAKER_00 4:56

It is an incredibly bright future. These tools are going to unlock so much human potential.

SPEAKER_01 5:01

100%.

SPEAKER_00 5:01

But here is the lingering question to keep you intellectually curious. When these AI agents become advanced enough to evaluate their own metrics and adjust parameters autonomously. Who is actually grading the grader? Well, if you enjoyed this deep dive, please subscribe to the show. Hey, leave us a five star review if you can. It really does help get the word out. Thanks for tuning in.