"You just let AI write your code?" — Yes. And here's why that makes me more of an engineer, not less.
The term "vibecoder" gets thrown around as an insult lately. It implies you're lazy, that you don't understand what's happening under the hood, that you're just prompting your way to technical debt.
I'm here to tell you something different: vibecoding isn't about replacing engineering. It's about removing friction between idea and revenue.
What Changed in Late 2024
For years, I built REST APIs the traditional way: Flask/Django, manual CRUD, boilerplate authentication, repetitive validation logic. Solid work. Reliable. Slow.
Then I hit a wall: a client needed a Telegram bot with AI symptom triage, 152-FZ compliance, PWA fallback, and 1C:Medicine integration — in 3 months.
Traditional approach: ~800 hours. Deadline: impossible.
So I adapted. I built workflows. I learned where AI accelerates and where human judgment is non-negotiable.
Result: 420 hours. Production launch. Zero compliance violations.
The 3-Tier Hybrid Pattern (With Code)
Here's the architecture I now use for every AI-powered system. It's not sexy, but it works in production.
Tier 1: Keyword Router (0ms, ~78–95% of requests)
# routers/keyword_router.py
import re
from typing import Optional, Callable
class KeywordRouter:
def __init__(self):
self.routes: dict[str, tuple[re.Pattern, Callable]] = {
"symptom_headache": (
re.compile(r"(голова болит|мигрень|цефалгия)", re.I),
self._route_neurology
),
"booking_cancel": (
re.compile(r"(отменить запись|отмена|перенести)", re.I),
self._route_appointment
),
}
def route(self, text: str) -> Optional[str]:
text_norm = text.lower().strip()
for route_name, (pattern, handler) in self.routes.items():
if pattern.search(text_norm):
return handler(text_norm)
return None
Why this matters: No LLM call. No latency. No API cost. 78% of medical queries and 95% of dating bot conversations are handled here.
Tier 2: RAG + Cache (~100ms, ~17% of requests)
# services/rag_service.py
from chromadb import Client
from sentence_transformers import SentenceTransformer
import hashlib
class RAGService:
def __init__(self, collection_name: str):
self.chroma = Client().get_collection(collection_name)
self.embedder = SentenceTransformer("rubert-tiny2")
self.cache: dict[str, str] = {}
def _normalize_key(self, query: str) -> str:
normalized = re.sub(r"[^\w\s]", "", query.lower().strip())
return hashlib.md5(normalized.encode()).hexdigest()
def get_response(self, query: str) -> Optional[str]:
key = self._normalize_key(query)
if key in self.cache:
return self.cache[key]
query_vec = self.embedder.encode([query])[0]
results = self.chroma.query(
query_embeddings=[query_vec.tolist()],
n_results=3,
include=["documents"]
)
if results["documents"][0]:
response = self._synthesize(results["documents"][0][0])
self.cache[key] = response
return response
return None
Tier 3: LLM Fallback (2–6s, ~5% of requests)
# services/llm_service.py
import asyncio
from llama_cpp import Llama
class LocalLLM:
def __init__(self, model_path: str):
self.llm = Llama(
model_path=model_path,
n_ctx=4096,
n_gpu_layers=0,
verbose=False
)
async def generate(self, prompt: str, timeout: float = 15.0) -> str:
try:
return await asyncio.wait_for(
asyncio.to_thread(self._generate_sync, prompt),
timeout=timeout
)
except asyncio.TimeoutError:
return "I'm taking too long — let me connect you to a human."
def _generate_sync(self, prompt: str) -> str:
output = self.llm(prompt, max_tokens=512, stop=["\n\n"])
return output["choices"][0]["text"].strip()
Where AI Helps — And Where It Doesn't
AI accelerates:
- Boilerplate CRUD endpoints
- Regex pattern generation
- Documentation drafts
- Test data generation
- Error message localization
Human judgment is non-negotiable:
- Architecture decisions (3-tier vs monolith)
- Compliance logic (152-FZ, GDPR, 323-FZ)
- Payment flows (idempotency, webhook verification)
- Error handling strategy
- Business logic edge cases
The Irony: More AI = More Engineering Discipline
The more I used AI, the more I realized: if your architecture is weak, AI helps you build broken systems faster.
So I built guardrails:
- No AI-generated code without human review (especially payments/auth)
- Architecture first, code second (sketch on paper before prompting)
- Test critical paths manually (AI can write tests, but I verify business logic)
- Error monitoring from day one (66 error codes catalogued in one project)
Results: Speed Without Sacrificing Reliability
| Metric | Traditional | AI-Augmented |
|---|---|---|
| Delivery time | 8 weeks | 3.5 weeks |
| Boilerplate hours | ~300 | ~40 |
| Production bugs (first month) | 12 | 3 |
| Uptime (first 6 weeks) | 98.1% | 99.7% |
The projects didn't just ship faster — they shipped better, because I spent time on architecture instead of typing @app.post("/users") for the 47th time.
Vibecoding Is a Philosophy, Not a Shortcut
Call it what you want: AI-augmented engineering, high-velocity development, vibecoding. The label doesn't matter. The outcome does.
I'm not here to win arguments on Twitter. I'm here to ship products that make money — with production-grade reliability, compliance, and maintainability.
Built AI-powered systems for healthcare, luxury tourism, and social tech. Portfolio: grekcreator.com
Top comments (2)
The 3-tier routing pattern is the part worth highlighting more — keyword → RAG → LLM fallback is genuinely good architecture, not vibecoding. Most people skip to LLM for everything and then wonder why it's slow and expensive. The irony is AI-assisted development pushed you toward a more disciplined system than you'd have built otherwise. That tracks with my experience too.
Spot on. AI didn't replace discipline — it demanded more of it. Thanks for reading!