AI Engineering

A blog series

AI Engineering for Full-Stack Developers

Building LLM features you can trust: the OWASP Top 10 for LLM apps, prompt injection and safe tools, evaluation harnesses, cost and latency, and observability with guardrails. Every demo uses a deterministic fake model, so nothing needs an API key.

Started 
2026
Lessons 
5
Reading 
~1 h

Contents

Every lesson has the same steps: what it is, how it works, a real example, common mistakes, then interview questions with answers you can say out loud.

  1. Lesson 00Securing LLM Applications: The OWASP Top 10 for LLM Apps in CodeThe ten 2025 OWASP risks for LLM applications, each with a plain definition, a vulnerable sketch, a fix and a one-line interview answer.14 min
  2. Lesson 01Prompt Injection and Safe Tool Use for AgentsDirect and indirect injection, a mock agent leaking private notes, and a policy layer that blocks the leak.13 min
  3. Lesson 02Building an Evaluation Harness for an LLM FeatureA golden set, code checks and a mock judge, confidence intervals, and a CI gate that catches a regression.14 min
  4. Lesson 03Cost, Latency and Caching for LLM FeaturesToken arithmetic, time to first token, prompt and response caching, model routing, batching, timeouts and budgets, with a simulation.20 min
  5. Lesson 04Observability and Guardrails for LLM Features in ProductionWhat to log and what never to log, spans, redaction, schema validation, a circuit breaker, a kill switch, prompt versions and an incident playbook.18 min

Glossary

15 terms used in this series. Inside any lesson, hover or tap a dotted term to see its meaning without leaving the page. Browse all terms →