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Safe AI for Developers: Shipping AI Features Without Shipping the Risk

The Safety Requirements That Land on Engineering Teams

Quiz: The Safety Requirements That Land on Engineering Teams

Threat Modelling an AI Feature Before You Write Code

Quiz: Threat Modelling an AI Feature Before You Write Code

Prompt Injection, Tool Abuse and Untrusted Content Handling

Quiz: Prompt Injection, Tool Abuse and Untrusted Content Handling

Secrets, Personal Data and What Leaves Your Trust Boundary

Quiz: Secrets, Personal Data and What Leaves Your Trust Boundary

Designing Evaluation Gates Into Continuous Integration

Quiz: Designing Evaluation Gates Into Continuous Integration

Writing Test Sets That Represent Real Users and Real Failures

Quiz: Writing Test Sets That Represent Real Users and Real Failures

Guardrails at Input, Output and Tool Call Level

Quiz: Guardrails at Input, Output and Tool Call Level

Structured Output, Schema Validation and Refusal Handling

Quiz: Structured Output, Schema Validation and Refusal Handling

Retrieval Design That Reduces Fabricated Answers

Quiz: Retrieval Design That Reduces Fabricated Answers

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Quiz: Retrieval Design That Reduces Fabricated Answers

Safe AI for Developers: Shipping AI Features Without Shipping the Risk
Retrieval Design That Reduces Fabricated Answers
Quiz: Retrieval Design That Reduces Fabricated Answers

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Question 1 of 5

1. Question

Why is fabrication a material risk for AI applications that provide factual information?

It is not a risk; models never fabricate
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Models can generate false information that appears confident and contextually appropriate, misleading users
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Fabrication only occurs in training data, not in inference
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Users always verify model outputs
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Question 2 of 5

2. Question

How does retrieval-augmented generation reduce fabrication?

It does not; RAG makes fabrication worse
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By grounding model answers in external documents you control, so answers come from a verified source of truth rather than training data
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By making the model faster
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By requiring users to check all answers
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Question 3 of 5

3. Question

Why should the amount of retrieved context passed to a model be limited?

To make the system faster
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Because large amounts of context confuse the model about which parts are most relevant
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Because storing documents is expensive
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To comply with copyright law
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Question 4 of 5

4. Question

What is the purpose of requiring the model to cite sources in retrieval-augmented generation?

To make answers longer
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To allow verification that the answer is actually in the source documents, providing accountability
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To satisfy copyright holders
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Citations are optional and not necessary
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Question 5 of 5

5. Question

How can you measure retrieval quality and what does it mean for model accuracy?

Retrieval quality cannot be measured
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Measure how often retrieved documents contain the answer to the user's question; if quality is low, model answers will be fabricated
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Retrieval quality is separate from model accuracy
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Only downstream users can measure retrieval quality
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