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IntegrAuth Academy
Identity, security and practical AI — free, from zero. Byte-sized lessons, real diagrams, hands-on labs, zero fluff. Two programs, each with its own exam and certificate.
20 tracks 241 lessons 242 hands-on labs 230+ diagrams 100% free
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The big picture
Priya started with a chat box. Now she puts AI to work safely. Your goal: see how the pieces fit.
Key points
- The model only asks. Your code checks and runs every tool call.
- Tokens are money and time. A lean context saves both.
- People approve the risky steps. Give each tool only the access it needs.
- Know where your data goes. Secrets never go into an AI tool.
What Priya learned
- Safe AI at work: data and privacy: work data goes only into approved tools.
- Picking the right AI tool: start from the job and the data.
- Projects and custom assistants: instructions say how, files hold the facts.
- Using AI through an API: each request carries the whole chat.
- Cost, limits and caching: put stable parts first, so they can be cached.
- Tool calling and structured output: the model asks. Your code checks, then runs it.
- MCP: plug tools into AI: one standard plug. A new server is new software.
- Context and memory: keep the window lean. Put lasting rules in a file.
- Agents, subagents and skills: a model in a loop needs limits and a stop.
- Automations and agent builders: give AI one small job. A person approves what can't be undone.
- AI that uses your apps and browser: give the least access. The agent asks before it pays, sends or deletes.
- Coding assistants: take small steps, read every change, commit often.
- Guardrails and jailbreaks: check what goes in and out.
Where to go next
- Safe and responsible AI: prompt injection, scams, bias and AI rules.
- Coding with AI: the coding habits, taken further.
- Building with AI: build your own AI features.
The Cheat sheet & pop quiz is next.