Evely Perez

Focus on applied learning: choose a problem you care about (finance, healthcare, fashion, etc.) and learn the AI tools that solve it. Use no‑code/low‑code platforms at first (like Teachable Machine or Hugging Face Spaces) to get comfortable with concepts, then move to Python and libraries like scikit‑learn or PyTorch. Consistency matters more than perfection—code a little every day and keep a public repo as your portfolio.

Hey, fine-tuning saved my startup 90% on API costs here’s the simple path. Grab 1k customer chats/emails, clean in Google Sheets, use Unsloth on Colab free tier for Llama-3.1 8B with LoRA (trains in 90min). Deploy via HuggingFace Spaces. Pro tip: Synthetic data from GPT-4o-mini fills gaps cheap. Now handles domain-specific queries perfectly.

Decision tree: Can RAG hit 90% accuracy on held-out eval? → RAG + hybrid search. Still <90%? → LoRA/PEFT fine-tune (1% compute of the full). Need causal reasoning/style? → Full fine-tune. Prompting solo only for <100 examples, simple classification (OpenAI evals: CoT prompting closes 60% gap to tuned). Failure modes: RAG hallucination (bad chunks), fine-tune catastrophic forgetting. Always: evals first, A/B in prod, rollback ready. RAG is most pragmatic 80% of the time.

Flash is clearly designed to be the default in high-volume pipelines: you get Pro-grade reasoning on many tasks with 15%+ accuracy gains over 2.5 Flash on extraction benchmarks (handwriting, contracts, financial data), but with much lower latency and better price performance. That makes it ideal for: RAG-style Q&A, document and log parsing, long-context reasoning on mixed data, and orchestrating multi-step agents where you care about staying within per-user or per-tenant quotas. In practice, you promote a small set of ‘red zone’ tasks—hard math, very high-stakes decisions, or nuanced generation—to Pro, and let Flash handle 80–90% of routine reasoning.