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LLMs, Prompt Engineering & Context Engineering

LLM Safety and Reliability

Hallucination, prompt injection and guardrails — the risks to understand before an LLM app meets users.

Level
Intermediate
Lessons
3
Total time
45 min
Simulations
3
Included in LLMs, Prompt Engineering & Context Engineering · $9
Tools you'll use
  • Python
  • Claude
  • OpenAI
  • Gemini
Skills
  • Prompt injection
  • Grounding
  • Guardrails

What you will be able to do

  • Reduce hallucination with grounding and citations
  • Design systems that hold up against prompt injection

Before you start

A suggestion, never a lock — you can start here.

  • context-engineering
Modules that help first

Syllabus

3 lessons · 45 min

Every lesson's title and summary is public. The lessons themselves open once you own the track.

Every lesson in LLMs, Prompt Engineering & Context Engineering

  1. Hallucinations and Grounding

    15 min

    A model picks the most plausible token, not the truest one. The cure is not asking it to "not make things up" but putting the source in context, requesting checkable quotes, and allowing "not found".

    Included with the track Simulation

  2. Prompt Injection

    16 min

    The model reads your instructions and a product review through the same channel: tokens. When data contains commands, no prompt can separate them — what can is an architecture that limits the damage.

    Included with the track Simulation

  3. Guardrails and Monitoring

    14 min

    A guardrail is code that checks inputs and outputs without trusting the model. Monitoring is what tells you quality has dropped — before users do.

    Included with the track Simulation
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$9
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