AI engineering
From how a model generates a token to shipping a monitored feature in production — with a security module most courses skip entirely. Every module ends with evidence you can explain in an interview.
8 modules · 48 topics · 3 labs
The security module practices in our real AI Security labs today. Graded labs for the build modules are the next phase.
Graded labs unlock with the Academy Security plan or Pro.
CHECKPOINT 01
Explain how a language model produces output, and choose the right model for a task.
How LLMs generate text
Tokens, context windows and cost
Choosing and comparing models
Temperature and sampling
Strengths and failure modes
Hallucination and when to trust output
Portfolio proof
Benchmark three models on a real task and justify a choice on quality, latency and cost.
CHECKPOINT 02
Turn an unreliable prompt into a versioned, structured template with measured gains.
System vs user prompts
Few-shot and structure
Assembling context
Structured output and JSON
Prompt templates and versioning
Measuring a prompt change
Portfolio proof
Refactor a flaky prompt into a versioned, structured template and prove the improvement.
CHECKPOINT 03
Ground a model in your own documents and answer with citations you can trust.
Embeddings and similarity
Chunking real documents
Vector databases
Retrieval and re-ranking
Grounding and citations
Evaluating RAG quality
Portfolio proof
Build a RAG pipeline over your own docs that cites sources, and measure its grounding.
CHECKPOINT 04
Build an agent that plans, calls tools, and recovers from failure.
Function and tool calling
The agent loop
Designing tools and schemas
Multi-step planning
Subagents and orchestration
Retries and graceful failure
Portfolio proof
Build a tool-calling agent that completes a multi-step task and handles failures cleanly.
CHECKPOINT 05
Drive a coding agent to do real repository work, safely and under review.
Driving a coding agent
Project memory and context
Permissions and safe automation
Custom commands and hooks
MCP integrations
Reviewing agent output
Portfolio proof
Automate a real repo task end-to-end with a coding agent, gated by human review.
CHECKPOINT 06
Prove an AI feature works — and keeps working — with a real eval set.
Why evals matter
Building an eval set
Automatic vs human grading
Regression testing prompts
Cost and latency budgets
Hill-climbing quality
Portfolio proof
Build an eval suite for an AI feature and use it to prove a measurable improvement.
CHECKPOINT 07
Harden an AI feature against injection, unsafe tool use and data leakage — then prove it with adversarial tests.
Prompt-injection defenses
Output handling and sandboxing
Server-side tool authorization
Data leakage and PII
Guardrails and moderation
Red-teaming your own app
Portfolio proof
Harden an AI feature against injection and leakage, then verify with adversarial tests.
CHECKPOINT 08
Run an AI feature in production with cost control, reliability and monitoring.
Cost and rate-limit management
Caching and streaming
Observability for LLM apps
Fallbacks and reliability
Deployment and rollout
Monitoring drift and quality
Portfolio proof
Deploy an AI feature with caching, monitoring and a cost budget, then run a load test.
What this is
This is an original KnightVerse learning route. It does not reproduce another provider’s lessons. The security module practices in our deterministic AI Security labs, which are labelled as simulations; graded labs for the build modules are in development.
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