Your First Day as an AI Engineer

Your First Day as an AI EngineerFree

Fix a real AI feature in your first five minutes, and see what the job actually is.

Tokens: What Models Actually ReadFree

Models don't read words, they read tokens. Tokens decide your cost and your limits.

Next-Token Prediction & TemperatureFree

How an LLM picks each word, and the settings that control it.

How LLMs Are Built (and Why They Hallucinate)Free

Pretraining, instruction tuning and preference tuning, and what each stage means for your product.

Context Windows, Latency & CostFree

The three constraints behind every design decision in an AI product.

Anatomy of an LLM API CallPro

Roles, messages, and the fact that models remember nothing between calls.

Streaming ResponsesPro

Make AI apps feel instant: server-sent events, deltas and stop reasons.

Prompt Engineering That WorksPro

Clear instructions, examples, delimiters and output formats.

Reasoning Models & Chain of ThoughtPro

When to pay for thinking, how to prompt reasoning models, and tricks that boost accuracy.

Structured Output (JSON)Pro

Get machine-readable output and handle it safely when it breaks.

Tool Use (Function Calling)Pro

Let the model call your code: search, databases, APIs.

Embeddings & SimilarityPro

Turn meaning into numbers so you can search by meaning, not just keywords.

Chunking DocumentsPro

Split documents into retrievable pieces, without cutting the meaning apart.

Building a RAG PipelinePro

Connect indexing, retrieval and generation into one working system.

Hybrid Search & RerankingPro

Combine keyword and vector search, then rerank for precision.

Evaluating RAG: Recall, MRR & FaithfulnessPro

Measure retrieval and generation separately, so you know exactly what to fix.

Workflows vs AgentsPro

The five patterns you'll use most, and when NOT to build an agent.

The Agent LoopPro

Build the core loop behind every agent, with real guardrails.

Memory for Assistants & AgentsPro

Short-term context, long-term memory, and how to pick what to remember.

Multi-Agent SystemsPro

Orchestrators, subagents and handoffs, and why more agents isn't automatically better.

MCP: Model Context ProtocolPro

The open standard for plugging tools and data into AI apps.

Evals: Testing LLM SystemsPro

If you can't measure it, you can't safely change it.

Prompt Injection & GuardrailsPro

The #1 security risk in LLM apps, and how to reduce it.

Reliability, Cost & ObservabilityPro

Retries, caching, model routing and the metrics that matter in production.

Observability: Traces, Latency & CostPro

See inside every request: spans, p95 latency and cost per feature.

Cutting LLM Costs: Caching, Batching & RoutingPro

The levers teams use to cut AI bills by half or more, with real numbers.

Neural Networks in One LessonPro

Neurons, layers, activations and parameters: what's really inside the models you call.

How Models Learn: Gradient DescentPro

Loss, gradients and learning rate: the engine behind every neural network.

Data Splits, Overfitting & LeakagePro

Why a model can look perfect in development and fail in the real world.

Prompting vs RAG vs Fine-TuningPro

The decision every AI team faces, with a practical guide.

Pick an AI Feature People Pay ForPro

Find a problem worth solving with AI, and check the numbers before you build.

Call AI From Your App, SafelyPro

The backend pattern every AI product uses: keys on the server, validation, clean errors.

Protect User DataPro

What you can send to a model, what to redact, and how to earn trust with real customers.

Usage Limits & Pricing That Cover AI CostsPro

Quotas, rate limits and plans that keep heavy users from eating your margin.

AI UX People TrustPro

Design patterns that make AI features feel fast, honest and in the user's control.

Launch, Measure, ImprovePro

Ship a small first version, track the numbers that matter, and improve every week.