Scratchpad and Notes Fields — Your Model's Thinking Wallet (But Make It Practical)

You already learned how to outline-then-detail and how to design outputs for easy post-processing. Now let’s make the model’s thinking useful — not messy — for both humans and downstream systems.

What are scratchpad and notes fields? (Quick, practical definitions)

• Scratchpad: an internal working area where the model can perform intermediate reasoning, calculations, or experiments. Think of it as the model’s private whiteboard. Often used internally (hidden from the user) to improve final answers.

• Notes field: a visible field in the model's structured output where it records compact, human-readable reasoning, assumptions, or justifications. This is the transparent log you give to users or validation systems.

These are sibling tools. One is a lab coat the model uses while tinkering (scratchpad). The other is the neatly typed lab notebook you hand to the TA (notes).

Why this matters — building on what you already know

You’ve already seen:

• Outline-Then-Detail: break larger tasks into steps. Scratchpads are perfect for doing the steps.
• Post-Processing-Friendly Designs and Multi-Part Output Assembly: you learned to split outputs into parseable parts. Notes fields are literally one of those parts — designed for downstream consumption.

Use scratchpads to improve reasoning quality; expose a short, structured notes field to make the result reliable, auditable, and machine-friendly.

The pattern: Two-phase prompting (Do work, then report)

1. Tell the model to think internally in the scratchpad. Keep this private to avoid exposing chain-of-thought when you don’t want it.
2. After the internal work, require a concise notes field that summarizes assumptions, intermediate results, and a final answer structured for parsing.

Why two-phase? Because raw chain-of-thought is great for reasoning, but messy for downstream processes. Convert messy internal threads into neat outputs.

Practical examples (templates you can copy)

Example 1 — API-style prompt (pseudo):

You are given a problem. Use an internal scratchpad to compute. Do NOT show the scratchpad to the user. When finished, produce JSON with two keys: "answer" (final result) and "notes" (a 2-4 sentence summary of assumptions and key steps).

Problem: Compute the 7th Fibonacci number.

Expected model output (machine-friendly):

{
  "answer": 13,
  "notes": "Used iterative computation; sequence starts 0,1; intermediate values computed 2..12; no overflow concerns."
}

Example 2 — Visible notes field for audits (structured):

Produce:
- answer: string
- notes: {
    steps: ["brief step 1","brief step 2"],
    assumptions: ["assumption 1"],
    confidence: "low|medium|high"
  }

This makes downstream scoring and assembly trivial.

A tiny table: Scratchpad vs Notes (so you don’t blur lines)

Best practices — concise, practical rules

• Limit public verbosity. Notes should be short and structured. Save long chains-of-thought to scratchpads (if used).
• Use arrays for steps. steps: ["1: do X","2: do Y"] is easier to parse than a paragraph.
• Add a confidence indicator. Machines can weigh outputs more intelligently when they get a bump of meta-confidence.
• Enforce a token budget. Tell the model exactly how many sentences or tokens the notes may use.
• Canonicalize formats. If the notes include dates, numbers, or IDs, force formats (ISO dates, integer-only IDs).
• Make notes post-processing friendly. Include keys the downstream pipeline expects, e.g., trace_id, step_hashes, summary.

Safety, privacy, and evaluation considerations

• Chain-of-thought leakage: exposing scratchpad contents as notes can increase risk (prompt injection, revealing model behavior). Prefer internal scratchpads for private reasoning and short, sanitized notes for outputs.
• Evaluation bias: if annotators see the chain-of-thought, they may be biased by the model’s confidence or style. Use notes fields that are neutral and standardized for scoring.

Pro tip: If you must share reasoning for explainability, sanitize and compress it. Replace raw deliberation with a compact justification: the kind a doctor would write, not a stream-of-consciousness blog post.

Advanced patterns — when you want both transparency and performance

• Two-output pass: First pass — internal scratchpad to compute. Second pass — use the results to generate a notes field with structured steps and a cleaned answer. This reduces noisy exposure while keeping transparency.
• Step-checking loop: Ask the model to produce N numbered steps in the scratchpad, then validate each step with a short verifier prompt (fast, cheap). Collate verified steps into notes with pass/fail flags.
• Compact hashes for reproducibility: include a simple hash of critical intermediate values in notes so downstream systems can detect tampering or inconsistency.

Example output schema (JSON-like):

{
  "answer": "...",
  "notes": {
    "steps": ["step1 summary","step2 summary"],
    "assumptions": ["A","B"],
    "confidence": "high",
    "trace_hash": "abc123"
  }
}

(Yes, escape your quotes when you pass this to the model. The model is picky but forgivable.)

Quick checklist before you deploy

• Did you decide whether scratchpad content is internal or visible?
• Did you define the notes schema for downstream consumers?
• Did you enforce a token limit on notes and/or scratchpad use?
• Did you include confidence and trace metadata for auditing?
• Did you sanitize personally identifiable content in notes?

Closing (the mic-drop moment)

Scratchpads give your model the messy brainstorming space it needs to think well. Notes fields give your system the clean, machine-readable receipt it needs to trust results. Use them together like a good kitchen: scratchpad = stove and prep counter; notes = plated dish with a label. One cooks, one presents.

Key takeaways:

• Keep the scratchpad for internal, freeform reasoning. Use it to improve correctness.
• Keep notes short, structured, and post-processing-friendly. Make them auditable.
• Apply token budgets, standardized formats, and confidence metadata so both humans and systems can use the outputs reliably.

Go forth, build crisp outputs, and let the model think noisily — just don’t hand your users the unedited sticky notes of a caffeine-fueled brainstorming session.