> ## Documentation Index
> Fetch the complete documentation index at: https://docs.bitscale.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AP-6: Personalization at Scale

> Building personalization for 10 contacts is a design exercise. Building it for 500 contacts per week is an operations problem. Learn the QA systems and batch workflows that maintain quality at volume.

<Info>
  **AP-6 · AI Personalization · 125 XP · \~20 min**
</Info>

The systems you've built across AP-1 through AP-5 work beautifully for small batches. At 500 contacts per week, new problems emerge: AI failures, inconsistent quality, personalization that's technically correct but tone-deaf, and QA bottlenecks that kill the time savings you're trying to create.

This module covers the operational infrastructure for personalization at scale.

***

## The Failure Modes at Scale

As volume increases, these problems emerge:

| Problem                                     | Frequency at Scale | Impact                                           |
| ------------------------------------------- | ------------------ | ------------------------------------------------ |
| AI generates generic output despite context | 5–15% of rows      | Sounds automated; hurts reply rates              |
| Word count violations                       | 10–20% of rows     | Emails too long; lower reads                     |
| Hallucinated context                        | 2–5% of rows       | References things that aren't true; embarrassing |
| Missing personalization signals             | 15–30% of rows     | Drops to Level 1 without flagging                |
| Constraint violations                       | 5–10% of rows      | Wrong structure, wrong CTA, banned phrases       |

At 500 contacts/week, a 10% failure rate means 50 broken emails going to prospects. You need a QA layer.

***

## Building an Automated QA Layer

### QA Check 1: Word count

```
Count the words in this email body: {{email_body}}

Return: {"word_count": N, "within_limit": true/false, "limit": 90}
```

### QA Check 2: Constraint compliance

```
Review this email against these constraints:
Email: {{email_body}}

Check each constraint:
1. No banned phrases: "hope this finds you well", "quick question", "just checking in", "I wanted to reach out", "touch base"
2. First person singular only (no "we" unless referring to the company product in a specific sentence)
3. Single CTA only (one ask, not multiple)
4. No exclamation marks
5. No bullet points

Return: {"passes": true/false, "violations": ["list of any violations found"]}
```

### QA Check 3: Personalization depth

```
Evaluate the personalization depth of this email:
Email: {{email_body}}

Context provided to generate it:
- Signal used: {{signal_description}}
- Role challenge: {{primary_challenge}}
- Individual hook: {{individual_observation}}

Assess:
1. Does the email reference a specific signal? (yes/no — if yes, quote the specific reference)
2. Is the reference specific or generic? (specific = references the actual signal; generic = "companies like yours")
3. Personalization level: 1/2/3/4 (using AP-1 spectrum)

Return as JSON.
```

### QA Check 4: Hallucination detection

```
Review this email for factual claims that may be inaccurate:
Email: {{email_body}}

Source data used:
- Company: {{company_name}}
- Signal: {{signal_description}}
- Contact title: {{job_title}}

Flag any claims in the email that:
- Cannot be verified from the provided source data
- Make specific numerical claims not present in source data
- Reference events, people, or facts not in the source data

Return: {"hallucinations_detected": true/false, "flagged_claims": ["list any flagged phrases"]}
```

### Master QA score:

```
Given these QA check results:
- word_count_pass: {{word_count_pass}}
- constraint_pass: {{constraint_pass}}
- personalization_level: {{personalization_level}}
- hallucinations_detected: {{hallucinations}}

Assign overall QA status:
- approved: all checks pass, personalization level 3+
- approved_with_notes: passes compliance, but personalization level 1-2 (note to reviewer)
- needs_revision: any constraint violation OR hallucination detected
- reject: multiple failures or hallucination + wrong personalization

Return ONLY: approved, approved_with_notes, needs_revision, or reject
```

***

## Batch Review Process

Even with automated QA, spot-check 10% of approved emails manually. Build a review queue:

1. Filter `qa_status = approved_with_notes` → all get human review
2. Filter `qa_status = approved` → random sample 10% for spot-check
3. Filter `qa_status = needs_revision` → AI auto-retry with the same prompt (often fixes constraint violations)
4. Filter `qa_status = reject` → manual rewrite or discard

This process keeps review time under 20 minutes per 500-contact batch.

***

## Auto-Retry for Failed Emails

For `needs_revision` rows, build a retry prompt:

```
This email failed QA for the following reasons: {{qa_violations}}

Original email: {{email_body}}

Rewrite the email to fix these specific issues while keeping the personalization signals:
- Signal used: {{signal_description}}
- Role challenge: {{primary_challenge}}

Apply the same constraints as the original prompt.

Return ONLY the revised email body.
```

Run auto-retry once. If the retry also fails QA, flag for manual review — don't loop indefinitely.

***

## Scale Operations Checklist

For any outreach batch over 100 contacts:

* [ ] All 4 QA checks running as columns
* [ ] Master QA status column populated
* [ ] `needs_revision` rows: auto-retry column
* [ ] Review queue filtered: all `approved_with_notes` reviewed
* [ ] Spot-check: 10% of `approved` reviewed manually
* [ ] Final send list: only `approved` rows exported
* [ ] Send rate: no more than 50 new contacts/sending domain/day

***

<Tip>
  **Quick Check:** What are the four automated QA checks? What QA status triggers auto-retry? What percentage of approved emails should be spot-checked manually?
</Tip>

***

## AP-6 Challenge: Build the QA Infrastructure (+125 XP)

Take a batch of 50 AI-generated emails (from any previous challenge) and run them through the full QA system.

**Requirements:**

* All 4 QA check columns implemented
* Master QA status column
* Auto-retry column for all `needs_revision` rows
* Final send list (only approved rows)
* QA audit summary: what % passed on first attempt? What were the most common failure modes?

<Card title="Submit AP-6 Challenge →" icon="upload" href="https://bitscale.fillout.com/academy-challenge-ap6">
  Share your grid + QA audit summary. **+125 XP on approval.**
</Card>

***

<Card title="Next: AP-7 — AI Personalization Capstone →" icon="arrow-right" href="/academy/ai-personalization/capstone">
  Build and run a fully personalized outbound campaign with QA infrastructure. Earn the AI Personalization Specialist certification.
</Card>