Does an LLM output that looks grammatically "correct" mean it's linguistically sound? Often, no.

One of Turkish's most fundamental yet most overlooked mechanisms is vowel harmony and LLMs break it systematically specially with foreign proper nouns, abbreviations, and long inflectional chains.

To document this i put together a new case study: Vowel Harmony Breakdown and Morphological Well-Formedness as a Helpful-Criterion Failure.

The core claim is even when not a single word is technically "wrong," broken vowel harmony can directly violate the "Helpful" criterion of the HHH (Helpful, Honest, Harmless) framework because even accurate propositional content can fail to be usable, undermine trust in a formal document, or misfire in text-to-speech tools.

Five cases are examined:

Pronunciation-based (not spelling-based) suffix harmony on foreign proper nouns Reading-based allomorph selection on abbreviations Harmony attenuation across long inflectional chains Frequency-biased default allomorph selection on loanwords Breakdown of minor (rounding) vowel harmony in possessive-case chains
Full document is attached. Feedback and discussion is welcome from anyone.

AI solved the problem perfectly.

There was just one problem…

Nobody asked it to solve that problem. 😂

Sometimes AI doesn't fail because it's not smart enough.

It fails because we're solving the wrong problem.

Smart ≠ useful.

For every AI feature in your application, evaluate along four dimensions:
1. Accuracy: How correct and nuanced do the responses need to be?
2. Latency: What response time is acceptable for the user experience?
3. Cost: What's the per-request budget and monthly budget?
4. Context Length: How much input data needs to be processed per request?

Currently going through RAMP course and one thing that’s standing out to me

AI learning becomes much more useful when it connects to the work you already know.

Instead of just asking What can AI do?
RAMP makes you think: What can AI change in the way I work?

Still learning, but already looking at AI a little differently

Finished the capstone for the RAMP certification.

The idea: a company pastes a messy line like "need a React Native app with Razorpay checkout, six weeks, must work with our Django API." The system turns it into a proper work package. Scope document, expert shortlist with citations, milestone and payout terms.

Django, MySQL, Redis at the back. React Native in front, streaming the response.

The agents were the fun part. The proof layer was the real work.

Every AI-generated query has to pass a validator before it runs. Every match has to point back to where it came from. Every run gets logged with cost and latency. There is even a test where a bad prompt has to get blocked in front of the reviewer.

Which is fair. This thing is touching money, contracts and hiring.

Building it is easy now. Trusting it is not.

What actually Proof means ?

Certified in RAMP 🚀

A great learning experience that gave me a broader perspective on building and working with AI-native systems.

Excited to put it into practice.