# SME Knowledge Extraction: Turning Interview Transcripts into AI Memory

## The "Vanilla" Problem: Why AI Content Lacks Soul

If you ask ChatGPT to write an article about "Enterprise Cloud Migration," it will give you a technically correct, grammatically perfect, and utterly boring article. It will look exactly like the top 10 results on Google.

Why? Because it was trained on the "average" of the internet.

Your agency’s value isn't in reciting the average; it's in the specific, hard-earned wisdom of your Subject Matter Experts (SMEs). The war stories, the contrarian opinions, the specific metaphors they use to close deals.

The bottleneck is that SMEs hate writing. They don't have time to draft 2,000-word articles. But they *love* talking.

**DECA’s solution is simple:** Don't make them write. Make them talk. Then, use AI to turn that talk into structured memory.

***

## The Workflow: From Audio to JSON

We treat an SME interview not as "content creation," but as "data mining." Here is the pipeline:

### 1. The "Anti-Boring" Interview Protocol

Most interviews fail because the questions are too broad. If you ask an expert, "Tell me about cloud migration," they will give you a lecture.

You need **Constraint-Based Questioning** to trigger unique insights:

* **Instead of:** "What are the benefits of X?"
* **Ask:** "What is the biggest lie everyone in the industry tells about X?"
* **Ask:** "Tell me about a time this strategy failed miserably."
* **Ask:** "What is a counter-intuitive truth you’ve learned after 10 years?"

**Goal:** Elicit "Experience" (the extra 'E' in E-E-A-T) that doesn't exist in the training data.

### 2. The Extraction Engine (LLM-as-a-Miner)

Once you have the transcript, do not simply dump it into a writer bot. You must pass it through an **Extraction Agent**.

This agent doesn't write; it categorizes. It parses the messy transcript into structured data points.

**The Extraction Prompt Structure:**

> "Analyze the following transcript. Ignore small talk. Extract distinct 'Knowledge Blocks' in JSON format. Look for:
>
> 1. **Contrarian Opinions:** Views that oppose the consensus.
> 2. **Specific Metaphors:** Unique analogies the speaker uses.
> 3. **Hard Data/Stories:** Specific examples with numbers or outcomes."

### 3. The Output: Structured AI Memory

The result of this process is a clean JSON file that feeds directly into your **Knowledge Base** (see Article 8).

```json
{
  "topic": "Cloud Migration Costs",
  "source": "CTO Interview_2024-05",
  "insight_type": "Contrarian View",
  "summary": "Lift-and-shift is actually more expensive than on-prem in the first 12 months.",
  "direct_quote": "Everyone thinks the cloud is cheaper instantly. It's not. If you just lift-and-shift, you're renting a Ferrari to drive to the grocery store.",
  "metaphor": "Renting a Ferrari for grocery runs",
  "tags": ["cloud-costs", "mistakes", "aws"]
}
```

Now, this insight is "digitized." It is no longer trapped in a video file or the CEO's brain.

***

## Injecting the Soul

When you write the final draft (using the process from Article 11), you inject these JSON blocks into the prompt.

**Prompt Injection:**

> "Write a section on Cloud Costs. You MUST incorporate the following expert insight: \[Insert JSON Summary]. Use the metaphor of 'Renting a Ferrari' as described."

**The Result:**

* The AI writes with the *voice* of your expert.
* The content contains specific *nuances* that ChatGPT doesn't know.
* The article is unique because the *source data* is unique.

***

## Conclusion: The "Interview-First" Agency

In the DECA framework, writing is cheap. Thinking is expensive.

By shifting your focus from "writing content" to "mining experts," you solve the two biggest problems in AI content:

1. **Generic Quality:** Solved by unique inputs.
2. **SME Bandwidth:** Solved by requiring only 20 minutes of talking, not 4 hours of writing.

Your SMEs are the ore mines. DECA is the refinery. The content is just the metal that comes out the other end.

***

## FAQ

**Q: How long should the interviews be?** A: Short and focused. 15–20 minutes on a *single* specific topic yields better data than a 1-hour rambling session.

**Q: Can AI conduct the interview?** A: Yes. Voice-mode AI agents can interview your SMEs, ask follow-up questions, and automatically trigger the extraction workflow.

**Q: Does this work for technical industries?** A: It works *best* for technical industries where nuance and experience matter more than general facts.
