AI Engineering Judgment

The fundamental decision framework for when to use AI versus traditional code. Learn to evaluate problems across determinism, cost, latency, and maintainability axes — and stop defaulting to LLMs for everything.

Intent classification is the backbone of most real AI systems. Learn to build query routers that send different types of inputs to specialized handlers — with confidence thresholds, fallback strategies, and hybrid fast-path/LLM architectures.

Natural language to SQL is one of the highest-value AI applications — and one of the hardest to get right. Learn the architecture, schema injection strategies, query validation, error recovery, and when to use code generation instead of SQL.

Building coding assistants that generate, execute, test, and iterate on code safely. Learn execution sandboxing, validation loops, context management strategies, and where code generation helps versus where it introduces dangerous complexity.

AI systems break differently than traditional software. The same input can produce different outputs, bugs are probabilistic, and stack traces do not exist for reasoning failures. Learn the systematic approach to debugging non-deterministic AI systems.

Infinite loops are the most common failure mode in AI agents. Learn the four root causes — ambiguous tools, context pollution, missing stop conditions, and reasoning spirals — with concrete detection and prevention strategies.

RAG failures are either retrieval problems (wrong chunks retrieved) or generation problems (good context but bad synthesis). Learn to diagnose which stage is broken, fix common issues like chunk boundaries and embedding drift, and build a RAG debugging pipeline.

Most AI system latency comes from LLM calls and tool execution — not your code. Learn to profile every stage of an agent run, understand TTFT vs TPS, optimize streaming pipelines, and find the biggest wins by reducing LLM calls rather than optimizing code.

Token accounting reveals where your AI budget actually goes. Learn to track costs per user, per feature, and per conversation — then optimize with context trimming, model tiering, and caching to cut costs by 50-80% without sacrificing quality.

AI features fail when they break user trust. Learn to design products that set correct expectations, communicate uncertainty honestly, build trust through transparency, and handle failures gracefully — with real examples from production AI products.

Streaming transforms a 5-second wait into a 500ms perceived response. Learn to build progressive rendering components, skeleton states, cancellation UX, and partial result patterns that make AI features feel instant.

AI features fail in ways traditional software does not — rate limits, hallucinations, timeouts, tool failures, and model outages. Learn to design error boundaries, fallback strategies, and user-facing error messages that keep users productive even when the AI breaks.

Not every AI feature should be a chatbot. Learn when chat is the right interface (exploratory tasks, ambiguous queries) versus when structured UIs are better (form filling, dashboards, workflows) — with hybrid patterns that combine the best of both.

User feedback is the most valuable signal for improving AI systems — but only if you collect it effectively. Learn to design explicit feedback (thumbs up/down, corrections) and capture implicit signals (copy events, regeneration, abandonment) without annoying users.

Every prompt you send to an LLM API can contain personal data — user names, emails, addresses, and more. Learn PII detection, redaction before API calls, data residency requirements (GDPR, CCPA), provider retention policies, and anonymization strategies that keep you compliant.

AI systems can generate or be manipulated into producing harmful content. Learn to build input/output filtering pipelines, use dedicated safety classifiers, handle category taxonomies, and design escalation workflows for when automated moderation is not enough.

Regulated industries require audit trails for every AI decision. Learn what to log (inputs, outputs, model version, latency, cost, tool calls), how to structure traces for querying, how to provide explainability to end users, and retention policies that balance compliance with cost.

Your AI system depends on external APIs that can go down, deprecate models, change pricing, or alter terms of service. Learn to assess vendor risk, build abstraction layers, implement multi-provider failover, and plan exit strategies before you need them.

Estimate, allocate, and control token costs per request, per user, and per feature — with practical formulas, budget caps, and real-time usage tracking.

Cache similar (not just identical) queries by embedding similarity — a user asking 'What is LangGraph?' gets the cached response for 'Explain LangGraph' instead of a new LLM call.

Reduce input tokens without losing quality: conversation summarization, context pruning, document compression, and progressive detail reduction. Cut costs 40-60% on long conversations.

Detect, measure, and mitigate bias in LLM outputs — from demographic disparities in classification to stereotyped language in generation. Practical techniques for production agents.

Make AI decisions understandable: reasoning traces, source citations, confidence calibration, and showing your work — so users trust the agent and can verify its outputs.

Protect user data beyond PII filtering: informed consent for AI interactions, data retention policies, right to deletion, and minimizing data collection in agent systems.

Deploy AI agents responsibly: know when NOT to use AI, disclose limitations, design for graceful failure, and establish human oversight — the engineering judgment that prevents harm.