Code With Wilson

Prompt engineering is the practice of treating prompts as **software artifacts** rather than chat messages. A prompt has a structure, a version, an associated eval set, a cost profile, and a failure mode catalog. You refactor prompts, diff them, roll them back. The shift from "prompting" to "prompt engineering" happens the moment you stop tweaking strings in a playground and start measuring delta...

The shift from Chapter 3 to Chapter 4 is the shift from evaluating a *model* to evaluating a *system*. A model is a single function scored on a benchmark. A system is a pipeline of components, hit by real traffic, under joint constraints: latency, dollar cost, quality, safety — plus the variance of all four under load. **System eval treats those as a joint optimization, not a stack of independent...

Software testing assumes a deterministic spec — given input X, the function must return Y. LLM evaluation lives in a different universe: the same prompt produces different outputs across runs, two different outputs can both be correct, and "correct" often depends on context the test harness never saw. So eval is **not** "did the function return the right value?" It's **"across a representative dis...

Synthesis of the 49 LLM Fundamentals questions in the AI Engineer interview Q-bank. Goes deeper than Ch 2 — into the math interviewers actually probe. Covers: Transformer architecture, the attention mechanism (Q/K/V, multi-head, scaled dot-product, causal masking, GQA, MHA, Flash Attention), tokenization (BPE, WordPiece, SentencePiece), positional encoding (RoPE), embeddings, KV cache, context win...

A foundation model is the result of three ingredients combined in sequence: **a giant pile of data**, **an architecture that can learn patterns in that data efficiently**, and **a training process that runs for weeks on thousands of GPUs**. After pre-training, the raw model is shaped further with **post-training** to make it follow instructions and stay safe. To use the model in production, you al...

For most of the last decade, building anything intelligent meant collecting data, training a model, and shipping it. Foundation models broke that loop. Now most teams **consume** rather than **train** — and the new craft is everything wrapped around the API call. That craft, AI engineering, is a different discipline from ML engineering, with different skills, different bottlenecks, and different c...