Topic · 8 lessons in 3 series
AI and LLM apps
Building with language models safely: evaluation, prompt injection, cost and caching, guardrails, retrieval and agents.
Full-Stack QuestionsWhole series →
- LLMs, Agentic AI, RAG and HarnessesAgent vs agentic AI, workflows vs agents, the harness, MCP, RAG end to end, evaluation and safety.45 min
- Vector Databases, pgvector, Retrieval Evals and LLM CostDistance measures, HNSW and IVFFlat, the filter trap, hybrid search, golden sets, judges, prompt caching and tracing.40 min
Team Lead MindsetWhole series →
AI EngineeringWhole series →
- Securing 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
- Prompt 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
- Building 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
- Cost, 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
- Observability 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