Getting Started with Large Language Models: A Practical Guide for Engineers

The mental model engineers need

An LLM is a function from a sequence of tokens to a probability distribution over the next token. Everything else — chat, tools, agents, RAG — is engineering on top of that primitive. Treat the model as a stochastic component with a well-defined interface and unreliable outputs.

Choosing a model

For most production workloads the right starting point is a current-generation hosted frontier model accessed through a managed gateway. Open-weight models are the right choice when data residency, fine-tuning at scale, or unit economics push you off hosted pricing.

Benchmark on your own data. Public benchmarks are useful for screening; they are not a substitute for an evaluation harness that uses your actual prompts and expected outputs.

Retrieval-augmented generation done well

RAG fails when retrieval is wrong, when chunking destroys semantics, or when the prompt does not give the model permission to say it does not know. Fix retrieval first — embeddings choice, hybrid search, reranking — before you tune the prompt.

Cite your sources. A RAG system that surfaces the underlying documents alongside the answer is dramatically easier to debug and dramatically more trustworthy to users.

Evaluation and observability

Build an offline evaluation suite from day one. Combine deterministic checks — exact match, regex, schema validation — with LLM-as-a-judge for subjective qualities, and with human review for the highest-value flows.

Instrument every production call. You want traces that include the full prompt, the retrieved context, the model output, latency, and cost. Treat LLM calls as untrusted external dependencies and design for failure.