LLM Engineering.

LLM Engineering Learning Roadmap

A structured path from your first API call to building production-grade LLM applications — covering prompting, RAG, agents, fine-tuning, and safety.

Prompt Engineering

Write effective prompts using few-shot examples, chain-of-thought reasoning, role prompting, and structured output techniques.

LLM Engineering Projects

Projects that build real LLM-powered applications — from prompt engineering basics to production RAG systems and fine-tuned models.

Introduction to LLM Engineering

Understand how large language models work, learn the OpenAI API, and write your first production LLM application.

Intermediate

For developers with core concepts down

4 lessons

LLM Tool Use and Function Calling

Give LLMs the ability to call your functions, query databases, search the web, and take actions in the real world.

RAG — Retrieval-Augmented Generation

Build a production RAG pipeline: chunk documents, embed them, store in a vector database, retrieve on query, and generate grounded answers.

LLM Embeddings and Vector Databases

Generate text embeddings, understand semantic similarity, and build search and recommendation systems with vector databases.

LLM Context Engineering

Design effective context windows — managing conversation history, injecting knowledge, handling long documents, and optimizing token budgets.

Advanced

Production-grade patterns for experienced engineers

4 lessons

LLM Fine-Tuning

Understand when to fine-tune, prepare training data, run fine-tuning jobs, and evaluate the results.

LLM Evaluation

Measure LLM application quality systematically — reference-based metrics, LLM-as-judge, RAG evaluation, and regression testing.

LLM Production Patterns

Build production-ready LLM applications — rate limiting, caching, cost optimization, error handling, and observability.

LLM Safety and Guardrails

Build safe LLM applications — input validation, output filtering, jailbreak detection, content moderation, and safe system prompt design.