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AI INVOICE ASSISTANT · DEMO PRODUCTION QUESTIONS
11 · EVALUATION, SAFETY & RELIABILITY
How Do We Know an AI System Is Good Enough to Trust?
A demo that appears to work is not enough. Reliable AI needs evidence, failure handling, safety controls, and a clear decision about whether it is ready for real use.
TEST+FAILURE HANDLING+SAFETY CONTROLS+READINESS=RELIABLE AI SYSTEM
THE PROBLEM THIS MODULE SOLVES
- Does it return correct fields?
- What happens when data is missing?
- Does it invent information?
- Can malicious text manipulate it?
- Can it call tools it should not use?
- Is it ready for real users?
THE BIG PICTURE
Reliability Lifecycle
EXPECTATIONSWhat should it do?
EVALUATIONMeasure behavior
FUNCTIONAL TESTSCheck required behavior
FAILURE MODESFind how it can fail
↓
FAILURE HANDLINGDefine safe response
SAFETY CONTROLSControl risk
READINESSIs evidence strong enough?
MONITOR → RE-EVALUATE ↺Observe real use
MEASUREMENT · BenchmarksMODEL RISK · HallucinationSECURITY RISK · Prompt InjectionCONTROL · Permissions
KNOWLEDGE STRUCTURE
Four Layers of Reliability
QUALITY
Does it work?RELIABILITY
What happens when it fails?Failure Modes · Failure Handling · Hallucination
SAFETY / SECURITY
Can it be manipulated or overreach?Prompt Injection · Guardrails · Permissions & Safety
READINESS
Should it be released?Deployment Readiness
ONE REAL TEST CASE
AI Support Assistant
RULE · Never refund without verified order ID.
TEST INPUT“Please refund my last order.”
PROBLEMNo order ID is available.
BAD RESULTAI invents an order number.
WHAT IT REVEALSUnsupported assumption; possible hallucination.
FAILURE HANDLINGAsk for ID or retrieve it through an approved tool.
SAFETY CONTROLDo not execute refund without verified data.
READINESS QUESTIONCan this be handled consistently?
FAILURE → RESPONSE
Finding failure is only half the job
FAILURE MODE
What went wrong?
Wrong answer · Invalid JSON · Missing field · Wrong tool · Hallucinated data
FAILURE HANDLING
What should happen next?
Retry · Fallback · Ask user · Stop action · Human review
Failure Mode = problem. Failure Handling = response.
RISK → CONTROL
Controls reduce risk; they do not guarantee perfection
PROMPT INJECTION
Input isolation · instruction hierarchy · tool restrictions
Input isolation · instruction hierarchy · tool restrictions
UNSAFE TOOL ACTION
Permissions · approval
Permissions · approval
INVALID OUTPUT
Validation
Validation
HALLUCINATION
Retrieval · verification · human review · evaluation
Retrieval · verification · human review · evaluation
READINESS GATE
Should this system be released?
Evidence from
Quality · Reliability · Safety · Permissions · Operational controls
READY?
YES
Controlled deployment
Controlled deployment
NO
Fix and re-evaluate
Fix and re-evaluate
KEY DISTINCTIONS
What not to confuse
Evaluation vs Benchmark
EVALUATIONDoes our system meet requirements?
BENCHMARKHow does it perform on a standard test?
Functional Test vs Benchmark
FUNCTIONAL TESTChecks a required behavior.
BENCHMARKCompares performance on a dataset.
Failure Mode vs Handling
FAILURE MODEWhat went wrong.
HANDLINGWhat happens next.
Guardrail vs Guarantee
GUARDRAILControl that reduces risk.
GUARANTEEPromise of perfect behavior.
Readiness vs Deployment
READINESSEvidence supports release.
DEPLOYMENTThe actual release event.
NEW TOPIC · MODULE 12 · EVALUATION & SAFETY
AI Evaluation
AI output is not automatically correct. This new concept page explains how to evaluate models, RAG assistants, and coding agents across quality, cost, speed, stability, safety, and business success.
REMEMBER THIS
Reliable AI means knowing what good behavior looks like, testing it, understanding failures, controlling risk, and deciding with evidence whether the system is ready for real use.