I've reviewed dozens of LLM integrations at startups over the past two years. The same failure patterns keep showing up.

1. Treating prompts like they're static

Prompts drift. User inputs change. Models get updated. If your prompt isn't versioned and tested like code, you will ship regressions without knowing it.

2. No fallback strategy

Single-provider LLM calls with no retry logic or fallback model. One outage and your product is down. Build multi-provider routing from day one.

3. Skipping evals

"It looks good in the demo" is not a testing strategy. Without automated evals — even simple ones — you have no signal when quality degrades.

4. RAG without retrieval tuning

Most RAG setups have terrible recall. Chunking strategy, embedding model choice, and reranking are not optional — they're the product.

5. Ignoring inference costs until they're out of control

Token budgets, caching, model tiering — design these in from day one, not after your bill spikes.

If any of this sounds familiar, I help engineering teams fix exactly these problems. Senior AI/ML/LLM services, retainer-based.

→ whop.com/synthiq