LLM Engineering

LLM Engineering

From your first API call to production-grade LLM systems. Prompt engineering, RAG pipelines, vector databases, tool calling, fine-tuning, agents, evals, and everything you need to ship reliable, cost-efficient LLM applications.

BeginnerTopics 1–10
  • ·What Are LLMs
  • ·Your First API Call
  • ·Prompt Anatomy
  • ·Chat Completions
  • ·System Prompts
  • ·Temperature & Sampling
  • ·Tokens & Context Windows
  • ·Embeddings Basics
  • ·Structured Output
  • ·The RAG Pattern
Start Beginner →
IntermediateTopics 11–22
  • ·Few-Shot Prompting
  • ·Chain-of-Thought
  • ·Prompt Templates
  • ·Tool / Function Calling
  • ·RAG Pipeline Deep Dive
  • ·Vector Databases
  • ·Semantic Search
  • ·Streaming Responses
  • ·Structured Outputs with Pydantic
  • ·Context Window Management
  • ·Multi-Turn Conversations
  • ·Prompt Injection Defense
Start Intermediate →
AdvancedTopics 23–32
  • ·Fine-Tuning Basics
  • ·LoRA & QLoRA
  • ·Agent Design Patterns
  • ·Multi-Agent Systems
  • ·LLM Evaluation
  • ·Multi-Modal LLMs
  • ·Hallucination Mitigation
  • ·Retrieval Optimization
  • ·Knowledge Distillation
  • ·Embeddings Fine-Tuning
Start Advanced →
ProductionTopics 33–40
  • ·LLMOps Fundamentals
  • ·Cost Optimization
  • ·Latency Optimization
  • ·Observability & Tracing
  • ·Safety & Guardrails
  • ·Deployment Patterns
  • ·A/B Testing LLM Systems
  • ·CI/CD for LLM Apps
Start Production →