Day 4: Day 4

Today's Objective

Four extraction prompts: one that returns clean JSON from a contract, one that builds a comparison table from product descriptions, one that uses XML tags to structure complex multi-part analysis, and one that extracts structured data from unstructured customer feedback.

Getting Clean JSON from AI

The most common structured output request is JSON. The key is: define the exact schema you want, give an example, and tell the AI to return only JSON (no prose before or after).

contract_data_extraction___json.txt

CONTRACT DATA EXTRACTION → JSON

Extract the following information from the contract below. Return ONLY valid JSON matching this exact schema. Do not include any text before or after the JSON.

Schema:
{
  "parties": {
    "vendor": "string",
    "client": "string"
  },
  "contract_value": "number (annual, USD)",
  "start_date": "YYYY-MM-DD",
  "end_date": "YYYY-MM-DD",
  "auto_renewal": "boolean",
  "termination_notice_days": "number",
  "key_obligations": ["string", "string", "string"],
  "liability_cap": "string (e.g., '2x annual fees' or 'unlimited')"
}

If a field is not found in the contract, use null.

Contract:
[paste contract text here]

customer_feedback___json_array.txt

CUSTOMER FEEDBACK → JSON ARRAY

You are a customer feedback analyst. Extract structured data from the following customer feedback messages. Return a JSON array where each element has this structure:

{
  "feedback_id": "number",
  "sentiment": "positive | neutral | negative",
  "category": "product | support | pricing | onboarding | other",
  "urgency": "low | medium | high",
  "key_issue": "string (1 sentence)",
  "action_required": "boolean"
}

Return ONLY the JSON array. No explanations.

Feedback messages:
1. "The API documentation is excellent but rate limits are killing our use case."
2. "Onboarding took 3 weeks. Should take 3 days."
3. "Your support team responded in 4 minutes on a Saturday. Incredible."
4. "Pricing is fair but we wish there was a mid-tier option."

Comparison Tables from Unstructured Text

vendor_comparison_table.txt

VENDOR COMPARISON TABLE

You are a procurement analyst. one have three vendor proposals (pasted below). Create a comparison table with these exact columns:

| Criteria | Vendor A | Vendor B | Vendor C |
|---|---|---|---|
| Annual cost | | | |
| Implementation timeline | | | |
| SLA uptime guarantee | | | |
| Support tier included | | | |
| Data residency options | | | |
| Contract flexibility | | | |
| Strengths (1-2 words) | | | |
| Weaknesses (1-2 words) | | | |

Fill each cell with a short, specific answer. Use "N/A" if not mentioned. Do not add commentary before or after the table.

Vendor A proposal:
[paste here]

Vendor B proposal:
[paste here]

Vendor C proposal:
[paste here]

XML Tags for Complex Multi-Part Prompts

XML tags help the AI understand which part of a long prompt is which. They're especially useful when your prompt contains document input plus instructions, or multiple sections the AI needs to handle differently.

xml_tags___complex_analysis.txt

XML TAGS — COMPLEX ANALYSIS

You are a business analyst. This lesson is giving you a competitor announcement and our internal product roadmap. Analyze the competitive implications.

<competitor_announcement>
[paste competitor press release or announcement here]
</competitor_announcement>

<our_roadmap>
[paste your Q3/Q4 product roadmap here]
</our_roadmap>

<instructions>
Provide your analysis in this exact format:

OVERLAP: Which of our planned features does the competitor now offer?
DIFFERENTIATION: What do we still have that they don't?
ACCELERATION: Which roadmap items should we ship faster given this news?
RECONSIDERATION: Which roadmap items should we deprioritize or kill?
MESSAGING: One-paragraph suggested update to our competitive positioning.
</instructions>

Why XML tags work: The AI treats the content between tags as a unit. It won't confuse your instructions with your document content. This is especially important when your document input contains words that could look like instructions.

Build 4 Extraction Prompts for Your Work

Identify something you read regularly that contains structured information but isn't in a structured format (emails, reports, feedback, contracts, proposals)
Define the JSON schema you'd want for that information — what fields would make it useful?
Write and test a JSON extraction prompt on a real example from your work
Build one comparison table prompt for a recurring decision you face (vendor selection, option comparison, etc.)
Write one prompt using XML tags to separate document input from instructions

assList.remove('open')); }); (function(){ const bar = document.getElementById('readingProgress'); const body = document.querySelector('.lesson-body'); function update(){ if (!body) return; const rect = body.getBoundingClientRect(); const total = rect.height - window.innerHeight + rect.top; const scrolled = Math.min(Math.max(-rect.top, 0), total); const pct = total > 0 ? (scrolled / total) * 100 : 0; bar.style.width = pct + '%'; } window.addEventListener('scroll', update, { passive: true }); window.addEventListener('resize', update); update(); })();

What's Next

The foundations from today carry directly into Day 5. In the next session the focus shifts to Day 5 — building directly on everything covered here.

Supporting Videos & Reading

Go deeper with these external references.

YouTube

Day 4 — Video Walkthroughs Community tutorials and walkthroughs covering the concepts in this lesson.

→

YouTube

Day 4 Explained Deep-dive explanations and live-coding sessions from top educators.

→

Official Docs

Official Documentation Primary reference documentation for the technologies covered in this lesson.

→

GitHub

Open Source Examples Real-world codebases demonstrating the patterns taught in this lesson.

→

Day 4 Checkpoint

Before moving on, verify you can answer these without looking:

What is the core concept introduced in this lesson, and why does it matter?
What are the two or three most common mistakes practitioners make with this topic?
Can you explain the key code pattern from this lesson to a colleague in plain language?
What would break first if you skipped the safeguards or best practices described here?
How does today's topic connect to what comes in Day 5?

Live Bootcamp

Learn this in person — 2 days, 5 cities

Thu–Fri sessions in Denver, Los Angeles, New York, Chicago, and Dallas. $1,490 per seat. June–October 2026.

Reserve Your Seat →

Continue To Day 5

Day 5

→