← Builders
Step 5
You can trust it
Fine-Tuning & Adaptation
7 postsFine-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
How RAG actually works
LLM Infrastructure & Serving
6 postsPagedAttention 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
Model Selection
7 postsStop 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?