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LLM Engineering thumbnail

LLM Engineering

68 courses 6 categories

Part of Learn Data & AI

LLM engineering is the applied discipline of shipping production systems on top of large language models — the API-side, infra-side work that lives between prompt-writing and pre-training. Unlike the broader AI hub, this topic focuses narrowly on the provider side: building retrieval-augmented pipelines, designing agentic loops, evaluating model outputs at scale, defending against prompt injection, and keeping inference cost predictable. It is the engineering layer that turns a model API into a service that survives real traffic.

The toolchain in 2026 has stabilized around a recognizable stack. Orchestration is LangGraph, the OpenAI Agents SDK, CrewAI, or hand-rolled state machines. Retrieval lives on pgvector, Qdrant, Pinecone, Weaviate, or Turbopuffer with hybrid search and reranking via Cohere or open models. MCP (Model Context Protocol) has become the standard for exposing tools and resources to agents across providers. Evals run continuously through LangSmith, Braintrust, Langfuse, or in-house golden-dataset rigs, with LLM-as-judge for fuzzy assertions and exact-match for the rest.

What you'll find under this topic

  • RAG architecture: chunking strategies, embeddings, hybrid search, reranking, query rewriting
  • Agent design: tool-calling, state management, error recovery, multi-agent patterns
  • MCP servers and clients: exposing tools, resources, and prompts across providers
  • Production eval harnesses: regression suites, LLM-as-judge, trace-based debugging
  • Prompt-injection defense: input sanitization, output filtering, indirect-injection mitigation
  • Cost and latency control: model routing, prompt caching, structured outputs, batch API
  • Provider integration patterns: OpenAI, Anthropic, Gemini, open-weight via vLLM / Together

The hiring market for LLM engineers in 2026 includes every SaaS company with an AI feature roadmap, dedicated applied-AI teams at OpenAI, Anthropic, and Google, and a long tail of startups built on top of foundation models. The skill set is distinct from ML research and from generic backend work — it sits at the intersection.

Categories (6)

AI Agents thumbnail
AI Agents
AI agents are autonomous loops where a language model decides which tool or function to call next, runs it, observes…
AI App Building thumbnail
AI App Building
AI app building covers the work of turning an LLM API into a product that real users pay for. The category sits between…
LLMs & Fundamentals thumbnail
LLMs & Fundamentals
LLMs (large language models) are neural networks trained on enormous text corpora to predict the next token given a…
Model Context Protocol (MCP) thumbnail
Model Context Protocol (MCP)
Model Context Protocol (MCP) is the open standard Anthropic introduced in late 2024 for giving language models access…
Prompt Engineering thumbnail
Prompt Engineering
Prompt engineering is the discipline of writing instructions to language models that produce reliably-good outputs. The…
RAG (Retrieval-Augmented Generation) thumbnail
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is the architectural pattern that gives a language model access to your own…

Courses (68)

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Frequently asked questions

What does an LLM engineer actually do?
Designs prompts and system messages, builds RAG pipelines and agents, integrates models via API or self-hosted inference, writes evaluation harnesses and guardrails, controls cost and latency, defends against prompt injection, and works closely with product on what models can and can't reliably do. Most of the work is engineering around the model, not training it.
LLM engineering vs Prompt engineering — what's the difference?
Prompt engineering is a sub-skill — writing the actual instructions the model receives. LLM engineering is the broader role: prompts plus retrieval, evaluation, deployment, observability, cost, security, and orchestration. Pure prompt-engineering job titles have largely faded; the durable role is LLM engineer or AI engineer, with prompting as one component.
Do I need to understand transformers at the math level?
Not for applied LLM engineering — knowing what attention, tokens, embeddings, and context length mean conceptually is enough. Math-level understanding becomes relevant only if you're fine-tuning at scale, designing new architectures, or doing research. Most production LLM work succeeds on solid software engineering plus model literacy.
Closed models vs open weights — which to use?
Closed (OpenAI, Anthropic, Google, xAI) for the strongest quality, easy onboarding, and frontier capability. Open weights (Llama, Qwen, Mistral, DeepSeek) for cost at high volume, data residency, on-prem requirements, and full customization. Most production stacks mix both — frontier model for hard tasks, smaller open model for cheap high-volume calls.
How important are evaluations?
Critical and chronically underdone. Without an evaluation harness you can't tell whether a prompt change is an improvement or a regression, and prompt-engineering devolves into vibes-based iteration. Invest early in eval datasets, automated grading (model-as-judge or rule-based), and a way to compare runs side by side. This is where most LLM projects succeed or fail.

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