AI and LLM engineering

Most “LLM tools” advice is a list of ten frameworks and a SaaS logo wall. That is the shallow version. Building with LLMs is not picking a library; it is engineering the four things around the model, the prompt, the retrieval, the orchestration, and the evaluation, so the system does the same job correctly every time instead of impressively once. We run real LLM systems to operate a marketing agency, so this hub describes the engineering discipline, not the tool shelf. This is about constructing software with a language model, not about which chat product to buy.

What is LLM engineering, beyond calling an API?

It is treating the model as one unreliable component inside a system you control, and engineering the rest so the whole thing is dependable. A single API call gives you a clever answer you cannot trust twice; LLM engineering gives you a pipeline whose output you can ship. The work is making the model’s behaviour repeatable: a prompt that is specified instead of vibed, retrieval that feeds it the right facts so it stops guessing, orchestration that breaks a big task into checkable steps, and evals that tell you when a change made things worse. The honest framing we hold internally is that the model owns fluency and the engineer owns correctness. You are not prompting a chatbot; you are building a system that happens to have a language model in the middle.

What are the building blocks of an LLM system?

Four, and they map to the spokes of this hub. Prompt engineering is the contract: what the model is told, in what structure, with what examples, so behaviour is specified rather than hoped for. Retrieval, usually RAG (retrieval-augmented generation), grounds the model in your own facts so it answers from your data instead of inventing, the single biggest cure for hallucination. Orchestration is the wiring that turns one model call into a reliable multi-step workflow: tool use, agents, routing, retries, the part where a real task gets done. Evaluation is the discipline that makes the other three safe to change, a way to measure quality so you know an “improvement” did not silently break something. Skip evals and you are flying blind; skip retrieval and you are trusting the model’s memory; skip prompt discipline and every run is a coin flip.

How do you keep an LLM system reliable in production?

By engineering for the model’s failure modes instead of pretending they are gone. Models hallucinate, drift between versions, and behave differently on inputs you did not test, so reliability comes from the system, not the model. Ground answers in retrieval so claims trace to a source. Make every orchestration step a discrete, inspectable unit so a bad output traces to one stage, not a black box. Hold an eval set that runs on every prompt or model change, so a regression shows up before a user does. And keep a human gate on anything that makes a claim or carries the brand, the same line we hold in our own work: the machine owns consistency, a person owns truth. Reliability is not a model you pick; it is a discipline you build around whatever model you happen to be using.

What does building with LLMs look like end to end?

Take a builder shipping an internal assistant that answers from company docs. Prompt engineering sets the contract, the role, the format, the refusal rules, the examples. A RAG (retrieval-augmented generation) pipeline indexes the docs and feeds the relevant passages in at query time, so answers cite real sources instead of guessing. Orchestration chains the retrieval, the model call, and any tool steps into one workflow with retries and a fallback. An eval set of real questions runs on every change, so when you swap a prompt or a model you see the quality move instead of finding out from a complaint. None of it depends on the model being perfect, because the system is engineered around it. That is the same way we build the LLM systems behind our own agency, reliable because of the engineering around the model, not the model itself.

The deeper how-tos sit in the spokes: prompt engineering tools for the contract layer, RAG tools (retrieval-augmented generation) for grounding, and LLM orchestration for the wiring that turns model calls into reliable workflows.