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Builders · Step 5 of 5

You can trust it

Evaluation, guardrails, and the cost of being wrong.

20 pieces · start with the guide

A public benchmark measures the benchmark. Score the model on cases from your own product, and fix the ship line before you look.

The pieces in this step

Run it for real

Guide · 3 minMoving from local prototype to production serving — how to select, adapt, and run models once cost, latency, and reliability actually matter.

Fine-tune, RAG, or prompt: which one, and what each costs

Why LoRA works: the intrinsic-dimensionality story

The LoRA family: QLoRA, DoRA, and LoRA+ — which, and when?

LoRA hyperparameters demystified: rank, alpha, and what to set

Fine-tune your first model on free Colab: QLoRA in about 40 lines

Common fine-tuning pitfalls and how to debug them

PagedAttention and continuous batching: how one server answers more users

How LLM inference actually works: prefill vs decode

What quantization actually does: precision loss and vector-space collapse

MLP is the model's memory: where knowledge lives

A 70B model on one GPU: a practical guide to quantization

Where LLM serving costs actually go

Stop ranking LLMs, start profiling them

A practical checklist for picking an LLM for your feature

When no single model is enough: the primary and verifier pattern

Common failure modes in LLM systems — and how to catch them

The rule layer: deterministic guardrails around a probabilistic model

Evaluate models on your own set, not the public leaderboard

Grading what you can't exact-match

Questions beside this step

Off-the-shelf, or adapt your own?

Builders · Step 5The demo worked. Your production domain did not. Before you commit to training your own model, understand the adaptation ladder and why most teams stop far lower than they think they need to.