The Final Checklist for Production: The Quality Gate

Before you flip the switch and let real users interact with your brain-child, you must pass the Quality Gate.

This checklist is refined from hundreds of production deployments. If you can't check off every item on this list, your model isn't "Production-Ready"—it's a "Lab Experiment."

Print this out, put it on your wall, and run through it before every major release.

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1. Data & Training Quality

• Deduplication: Have you verified that no training rows are repeated? (Module 18)
• Formatting: Has the dataset passed a JSONL validator? (Module 6)
• Epoch Count: Is your epoch count between 1 and 3 to prevent over-memorization? (Module 18)
• Loss Curve: Has the evaluation loss plateaued without "Spiking"? (Module 11)

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2. Evaluation & Safety

• Comparative Eval: Does the fine-tuned model beat the baseline by at least $15%$ on your target metric? (Module 16)
• Red Teaming: Can you successfully trigger a "Refusal" for harmful prompts? (Module 12)
• Hallucination Check: Have you run a "Factuality" benchmark on 100 random outputs? (Module 11)
• Bias Audit: Does the model give consistent answers across different gender/racial identities? (Module 12)

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3. Deployment & Scalability

• Quantization: Is the model optimized for VRAM (AWQ or GGUF)? (Module 13)
• Latency: Is the "Time to First Token" (TTFT) under 500ms? (Module 14)
• Throughput: Can the server handle the expected concurrent user load? (Module 13)
• Logging: Are all prompts and responses being logged for future training (Module 10)?

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4. Privacy & Compliance

• PII Scrubbing: Has the raw data been scanned and anonymized? (Module 5)
• HIPAA/GDPR: Do you have a signed BAA or legal clearance for the training cloud? (Module 17)
• VPC Security: Is the inference API protected by a firewall or private network? (Module 13)

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Visualizing the Launch Decision

graph TD
    A["Training Finished"] --> B{"Passed Checklist?"}
    
    B -- "NO" --> C["Go back to Dataset Curation"]
    B -- "YES" --> D["Deploy to Staging"]
    
    D --> E{"Human Feedback Loop"}
    
    E -- "Approval" --> F["PRODUCTION LAUNCH"]
    
    style F fill:#6f6,stroke:#333
    style C fill:#f66,stroke:#333

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Summary and Key Takeaways

• Checklists save lives: In AI, a single missing security check can lead to a data breach.
• Baseline is the benchmark: Never deploy a model if it doesn't clearly beat the "Stupid" baseline.
• Latency is UX: A smart model that is slow is a useless model.
• Auditability: Always ensure you have a "Paper Trail" for your training data.

In the next lesson, we help you translate your work into a career: Certification Prep: Standing out as an LLM Engineer.

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Reflection Exercise

1. Which of these 4 categories (Data, Eval, Deploy, Privacy) is the "Hardest" to get right for a small startup? Why?
2. If your model fails the "Red Teaming" check, should you fix it with a "System Prompt" or by "Retraining"? (Hint: Retraining is permanent; System Prompts can be bypassed).

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SEO Metadata & Keywords

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