3.1 Introduction

Bringing large language models into the software development process is the next turn in the evolution of AI products. This section is a practical introduction to LLMOps, covering the full lifecycle of LLM‑based applications: from model selection and fine‑tuning to production deployment, monitoring, and ongoing operations. LLMs understand and generate human‑like text, so they are used for summarization, classification, content generation, and many other tasks. Their strengths are broad knowledge from training on large corpora, adaptability to a wide range of scenarios without heavy task‑specific training, and the ability to work with context and capture nuance. Building on this, LLMOps acts as the LLM‑focused layer of MLOps: model selection and domain preparation, thoughtful deployment to meet SLAs, continuous monitoring with metrics and alerts, plus security and privacy with ethical principles and data protection.

An LLMOps roadmap typically includes several steps. First, choose a model by size, training data, and benchmarks: match metrics to your task and prepare a fine‑tuning dataset that faithfully reflects the domain and goals. Next, design deployment architecture and infrastructure: plan for scale with headroom for peaks, minimize latency via caching and shorter execution paths, and account for integrations. In production, rely on continuous monitoring to catch degradation and data drift; define KPI/SLI up front, and bake in regular updates and regression tests. Throughout, protect privacy and security: anonymize sensitive fields, control access to models, prevent abuse, and formalize a responsible‑AI policy.

An LLM app’s structure typically involves selection and fine‑tuning: evaluate available options and their fit to your requirements, then adapt the model to your domain using prompt engineering, PEFT/LoRA, and other methods — paying attention to infrastructure compatibility and to the cost/efficiency balance of tuning techniques. Deployment is often a REST API around the model or an orchestrator; observability and real‑time metric tracking are critical to understand model health and react quickly to incidents. Automate anything repetitive: prompt management with versioning and A/B tests, automated tests and CI/CD, orchestration of multi‑step LLM chains and their dependencies. Data preparation underpins effective tuning: use SQL/ETL and open tooling to build clean data marts; orchestrate complex workflows to meet SLAs, with retries and idempotency as first‑class properties.

Best practices rest on three pillars: automation (tests and CI/CD speed up reliable releases), prompt management (context‑aware dynamics and steady A/B testing improve quality), and case‑by‑case scaling (a modular architecture that adds new scenarios without breaking existing ones, and capacity planning for load). Given how fast LLMs and MLOps change, build in flexibility: follow trends, engage with the community, and regularly take courses and workshops.

From practice: automating support with an LLM chatbot plus dynamic prompt management reduces response time and improves service quality; in publishing, a summarization‑and‑editing pipeline together with prompt management radically speeds article production. Overall, a structured approach to LLMOps — with automation, solid prompt management, thoughtful scalability, and a culture of continuous learning — is key to building and operating successful LLM applications. For deeper study, keep these at hand: WhyLabs’ “A Guide to LLMOps” with material on prompts, evaluation, testing, and scaling; Weights & Biases “Understanding LLMOps” — a review of open and proprietary LLMs with monitoring practices; and the DataRobot AI Wiki, which positions LLMOps as a subset of MLOps and covers adjacent topics.