Production

Concept

Context Engineering Explained

Prompt engineering is one input. Context engineering is everything around it — what you fetch, what you trim, what you remember, what you let the model see.

Concept

Guardrails & Output Validation

LLMs don't come with guarantees. Guardrails and output validation are how you enforce safety, structure, and policy on probabilistic outputs before they reach users.

Concept

Human-in-the-Loop Automation Patterns

When to require human approval in AI automations — approval gates, review queues, selective auto-send, and the metrics that tell you when to loosen the loop.

Concept

Idempotency and Retries for LLM Workflows

How to retry failed LLM and tool calls without double-charging customers, sending duplicate emails, or corrupting agent state.

Concept

LLM Cost Optimization

The operational playbook for cutting LLM spend without cutting quality. Model routing, caching, batching, and the metrics that actually matter.

Concept

LLM Observability in Production

You can't debug what you can't see. LLM observability means tracing every prompt, token, latency spike, and failure — here's the production stack.

Concept

Structured Outputs Explained

Structured outputs make models return valid JSON every time — no more parsing failures, no more 'almost JSON.' Here's how they work and when to use them.

Concept

When NOT to Use AI

The honest guide to tasks where AI makes things worse, not better. Know when to skip the LLM and use a spreadsheet, a SQL query, or a human instead.

Tool Guide

Langfuse Guide

The definitive guide to Langfuse for LLM observability — traces, prompt versions, evals, cost tracking, self-host setup, and when to pick it over Braintrust or Phoenix.

Workflow

Build an AI Docs Chatbot

A RAG-powered chatbot that answers questions from your documentation with citations — the kind every SaaS site needs in 2026.

Case Study

Case Study: RAG Docs Chatbot Shipped in 3 Weeks

How a 12-person B2B SaaS team shipped a cited docs chatbot that deflected 34% of support tickets — without hiring an ML team.

Case Study

Case Study: Support Ticket Triage + Draft Reply Automation

How a customer-support team auto-classified 2,000 tickets/day, drafted first replies for 60% of them, and cut median first-response time from 4 hours to 12 minutes.

Interview

How do you decide which tools to give an AI agent, and how many is too many?

Tool design is the main lever for agent reliability — bad tool sets cause wrong calls, loops, and security holes.

Interview

How would you build an evaluation suite for an LLM-powered feature?

Evals are the difference between shipping and praying. Standard interview topic in 2026.

Interview

An LLM hallucinated a fake API in production code. How do you prevent this systematically?

Tests whether you treat LLM reliability as an engineering problem, not a model problem.

Interview

What caching strategies do you use to cut LLM latency and cost in production?

Separates candidates who've run LLM apps at scale from those who only call APIs in notebooks.

Interview

How do you handle PII in a RAG pipeline without breaking retrieval quality?

Tests privacy-aware AI design — a gap between demo RAG and enterprise deployment.

Interview

How do you defend a production LLM application against prompt injection attacks?

Tests whether you treat LLM security as layered engineering, not a single prompt trick.

Interview

Walk me through designing a production RAG system for a docs chatbot.

System-design-style question that's now standard in AI engineering interviews.

Interview

When would you use RAG vs fine-tuning to give an LLM access to new information?

Tests practical LLM judgment — this is the #1 question in applied AI interviews in 2026.

Interview

Your LLM keeps returning invalid JSON for structured outputs. How do you fix it systematically?

Tests production reliability — structured outputs power agents, evals, and integrations; failures can't be hand-waved.