Introduction to LLM Engineering

The LLM engineer role, product development lifecycle for AI apps, and key decisions separating prototypes from production.

Intermediate · 12 min read

What is LLM Engineering?

LLM Engineering builds reliable, production-grade applications powered by large language models. Unlike ML research, it focuses on product outcomes: Does the user's problem get solved? Is it fast enough? Does it fail gracefully?

ML Engineer LLM Engineer
Trains models from scratch Orchestrates pre-trained models
Writes loss functions, backprop Writes prompts, RAG, agents
Optimizes GPU training Optimizes latency, cost, reliability
Evaluates with benchmark metrics Evaluates with human test cases

Flow:

  1. Plan — Define problem, success metrics
  2. Prompt — Engineer and test prompts
  3. Prototype — Build with Streamlit
  4. Evaluate — Test cases, LLM-as-judge
  5. Ship — API, observability, monitoring

Part of the Speech Recognition & LLM Engineering series on Tekivex. Browse all tutorials or explore our open-source products.