Phase 1 — LLM Vocabulary and Mental Models
The vocabulary phase. By the end you can read any model card, pricing page, catalog row, serving dashboard, or local-model page without looking anything up — and you have the one mental model everything else hangs on.
Why this phase comes first
Every later phase (serving, gateways, RAG, agents, evaluation, startup economics) assumes this vocabulary. Engineers who skip it memorize disconnected facts and make expensive mistakes. Engineers who master it can reason about a brand-new technique on sight, because they can place it in the inference pipeline and the Six Laws.
Documents
| # | Document | What you'll be able to do |
|---|---|---|
| 00 | Core Mental Model | Explain the token loop and the Six Laws of LLM engineering |
| 01 | Tokenization and Context | Count tokens, budget a context window, estimate cost |
| 02 | Parameters, Weights, and Checkpoints | Read "8B / 70B / 26B-A4B", predict memory, dense vs MoE |
| 03 | Inference Parameters | Tune temperature/sampling/max_tokens for any task |
| 04 | Model Capabilities | Decode base/instruct/reasoning/multimodal/embedding/tools |
| 05 | Serving Terms | Reason about prefill/decode, KV cache, TTFT/TPOT, throughput |
| 06 | Local Model Terms | Pick formats (GGUF/safetensors), quantization, and runtimes |
| 07 | Evaluation Terms | Distinguish benchmarks from evals; measure real quality |
| 08 | Business and Pricing Terms | Model unit economics, routing, fallbacks, providers |
| 09 | Complete Glossary | Look up all 170+ terms in one place |
How to work through it
- Read 00 first — it frames everything.
- Read 01–08 in order; each follows the same 15-section template (Why → Core Concept → Mental Model → Hitchhiker's Guide → Warmup/Deep readings → Key Terms → Facts → Real Systems → Misconceptions → Decision Framework → Lab → Verification → Takeaways → Artifacts).
- Do every lab. The labs build a reusable toolkit (token counter, cost/margin calculator, eval harness, local endpoint) you'll extend in later phases.
- Use 09 as your ongoing reference and self-test.
Phase 1 artifacts (what you should have produced by the end)
- A token counter + cost/margin calculator
- A parameter playground and a memory/"can-I-run-it" estimator
- A capability classifier and a tool-calling reliability check
- A latency-vs-concurrency benchmark against a real endpoint
- A working local OpenAI-compatible endpoint (Ollama/llama.cpp)
- A minimal offline eval harness with a golden set
- Notes + cheat cards: the Six Laws, the cost formula, quantization variants