AI Data Labeling Brief Template: Scope and Manage Annotation Projects

Data labeling is the foundation of supervised AI products. Poor labeling leads to poor models, wasted compute, and frustrated users. Yet most AI PMs scope labeling projects on gut feel rather than structured briefs. This template ensures every annotation project starts with clear requirements, measurable quality targets, and realistic cost expectations.

Why Data Labeling Briefs Matter

Common Labeling Failures Without a Brief

Ambiguous Guidelines

Annotators interpret tasks differently, creating noisy labels

Scope Creep

Label taxonomy expands mid-project, invalidating earlier work

Quality Drift

No quality benchmarks means degradation goes undetected

Budget Overruns

Rework and scope changes push costs 2-3x above estimates

Data Labeling Brief Template

Copy and customize this template for your annotation projects:

╔══════════════════════════════════════════════════════════════════╗ ║ AI DATA LABELING BRIEF DOCUMENT ║ ╠══════════════════════════════════════════════════════════════════╣ PROJECT OVERVIEW ──────────────────────────────────────────────────────────────────── Project Name: [Name of labeling project] Project Lead: [PM Name] ML Engineer: [Engineer Name] Start Date: [YYYY-MM-DD] Target Completion: [YYYY-MM-DD] Model Use Case: [What model will these labels train?] DATA SPECIFICATION ──────────────────────────────────────────────────────────────────── Data Type: [Text / Image / Audio / Video / Multi-modal] Total Samples: [Number of items to label] Source: [Where data comes from] Format: [File format - JSON, CSV, PNG, WAV, etc.] Sensitive Data: [Yes/No - PII, medical, financial?] Storage Location: [S3 bucket, GCS, local, etc.] ANNOTATION TASK DEFINITION ──────────────────────────────────────────────────────────────────── Task Type: [ ] Classification (single label) [ ] Classification (multi-label) [ ] Named Entity Recognition (NER) [ ] Bounding Box Detection [ ] Semantic Segmentation [ ] Sequence Labeling [ ] Ranking / Rating [ ] Text Generation / Paraphrase [ ] Other: _______________ LABEL TAXONOMY ──────────────────────────────────────────────────────────────────── Label Definition Example ──────────────────────────────────────────────────────────────────── [Label 1] [Clear definition] [Concrete example] [Label 2] [Clear definition] [Concrete example] [Label 3] [Clear definition] [Concrete example] [Label N] [Clear definition] [Concrete example] Edge Cases & Decision Rules: • If [ambiguous scenario 1] → Apply [Label X] • If [ambiguous scenario 2] → Apply [Label Y] • If [uncertain/unclear] → Flag for review • If [multiple labels apply] → [Priority rule] ╠══════════════════════════════════════════════════════════════════╣ ║ QUALITY ASSURANCE ║ ╠══════════════════════════════════════════════════════════════════╣ INTER-ANNOTATOR AGREEMENT TARGET ──────────────────────────────────────────────────────────────────── Metric: [Cohen's Kappa / Fleiss' Kappa / % Agreement] Minimum Threshold: [≥ 0.80 recommended for production] Annotators per Item: [2-3 recommended] Adjudication: [Majority vote / Expert review / Discussion] QUALITY GATES ──────────────────────────────────────────────────────────────────── Gate Trigger Action ──────────────────────────────────────────────────────────────────── Pilot Batch First 50 items Review IAA; refine guidelines 10% Audit Every 10% complete Spot-check 5% of batch Flagged Items Any annotator flags Expert adjudication within 24h Final QA Project complete Full statistical review GOLD STANDARD SET ──────────────────────────────────────────────────────────────────── Size: [50-100 expert-labeled items] Created by: [Domain expert name] Inserted as: [X% of each annotator's queue, hidden] Pass Threshold: [≥ 90% accuracy on gold items] Fail Action: [Retrain annotator / Remove from project] ╠══════════════════════════════════════════════════════════════════╣ ║ VENDOR & RESOURCING ║ ╠══════════════════════════════════════════════════════════════════╣ LABELING APPROACH ──────────────────────────────────────────────────────────────────── [ ] In-house team [ ] External vendor (e.g., Scale AI, Labelbox, Appen) [ ] Crowdsource (e.g., MTurk, Toloka) [ ] AI-assisted (model pre-labels + human review) [ ] Hybrid: _______________ Vendor/Tool: [Platform name] Annotator Count: [Number of annotators needed] Domain Expertise: [Required/Preferred/Not needed] Language Req: [Languages annotators must speak] NDA Required: [Yes/No]

Cost Estimation Framework

Use this framework to estimate your labeling project budget:

COST ESTIMATION WORKSHEET ──────────────────────────────────────────────────────────────────── Volume & Throughput ──────────────────────────────────────────────────────────────────── Total samples: [N] Avg time per label: [X minutes] Labels per annotator per hour: [N / X * 60] Total annotator hours: [N * X / 60] Cost Calculation ──────────────────────────────────────────────────────────────────── Line Item Rate Total ──────────────────────────────────────────────────────────────────── Primary labeling $[X]/hr $[...] Multi-annotator overlap [X]x $[...] Gold set creation [X] hrs $[...] QA & adjudication [X]% of base $[...] Platform/tooling fees $[X]/month $[...] Project management [X] hrs $[...] ──────────────────────────────────────────────────────────────────── SUBTOTAL $[...] Contingency buffer (20%) $[...] TOTAL BUDGET $[...] Cost Benchmarks by Task Type ──────────────────────────────────────────────────────────────────── Task Type Cost/Item Throughput ──────────────────────────────────────────────────────────────────── Text classif. $0.02-0.10 200-500/hr NER $0.05-0.20 50-150/hr Bounding box $0.10-0.50 30-100/hr Segmentation $0.50-2.00 10-30/hr Text generation $0.20-1.00 20-60/hr Audio transcription $0.50-2.00 10-30/hr

Annotation Guidelines Template

Create a separate guidelines document using this structure:

ANNOTATION GUIDELINES ──────────────────────────────────────────────────────────────────── 1. TASK SUMMARY What you are labeling: [One sentence description] Why it matters: [How labels will be used] 2. STEP-BY-STEP INSTRUCTIONS Step 1: [Read/view the full item] Step 2: [Identify the key feature/attribute] Step 3: [Apply the label from the taxonomy] Step 4: [If uncertain, flag for review] Step 5: [Move to next item] 3. LABEL DEFINITIONS WITH EXAMPLES LABEL A: [Name] ✔ Definition: [Precise definition] ✔ Include when: [Positive criteria] ✘ Exclude when: [Negative criteria] ✔ Example 1: [Clear positive example] ✔ Example 2: [Borderline positive example] ✘ Counter-example: [Looks like A but is NOT] LABEL B: [Name] ✔ Definition: [Precise definition] ✔ Include when: [Positive criteria] ✘ Exclude when: [Negative criteria] ✔ Example 1: [Clear positive example] ✔ Example 2: [Borderline positive example] ✘ Counter-example: [Looks like B but is NOT] 4. EDGE CASE DECISION TREE Is the item [condition 1]? └─ Yes → Apply [Label X] └─ No → Is it [condition 2]? └─ Yes → Apply [Label Y] └─ No → Flag for expert review 5. COMMON MISTAKES ✘ [Mistake 1]: [Explanation of why it's wrong] ✘ [Mistake 2]: [Explanation of why it's wrong] ✘ [Mistake 3]: [Explanation of why it's wrong]

Project Timeline Template

Recommended Labeling Project Phases

Week 1

Setup & Pilot

Finalize taxonomy, create gold set, onboard annotators, run pilot batch of 50 items

Week 2

Calibration

Review pilot IAA, refine guidelines, retrain annotators on edge cases, approve for full production

Weeks 3-N

Production Labeling

Full-speed annotation with 10% batch audits, weekly QA reviews, ongoing edge case documentation

Final Week

QA & Delivery

Final statistical review, adjudicate all flagged items, export labeled dataset, document lessons learned

AI-Assisted Labeling Checklist

When to Use Model Pre-Labeling

You have an existing model with > 70% accuracy on the task

Task is well-defined with clear label boundaries

Volume is large enough (> 5,000 items) to justify setup cost

Warning: Pre-labels create anchoring bias. Annotators tend to accept model suggestions. Mitigate by hiding confidence scores and randomizing pre-label display.

AI-ASSISTED LABELING SETUP ──────────────────────────────────────────────────────────────────── Pre-label Model: [Model name and version] Pre-label Accuracy: [X% on validation set] Confidence Threshold:[Items above X% auto-accepted] Routing Rules: • High confidence (≥ 95%): Auto-accept, 5% human audit • Medium confidence (70-94%): Single human review • Low confidence (< 70%): Dual human annotation • Edge cases: Expert review queue Expected Efficiency Gain: • Without AI assist: [X] items/hour • With AI assist: [Y] items/hour • Cost savings: [Z]%

Common Data Labeling Mistakes

Skipping the Pilot Batch

Going straight to full production without testing guidelines on 50 items first. Always run a pilot to catch ambiguities before they compound across thousands of labels.

Vague Label Definitions

Definitions like "positive sentiment" without specifying what counts as positive. Every label needs a precise definition, 2+ examples, and at least one counter-example.

Single Annotator Per Item

Using one annotator per item with no overlap. You cannot measure agreement, detect bias, or identify struggling annotators without overlap on at least 10-20% of items.

Ignoring Class Imbalance

If 95% of items are one class, annotators develop a "default label" habit. Stratify queues to ensure annotators see balanced distributions, then weight results accordingly.

No Versioning

Updating guidelines mid-project without versioning. Earlier labels may be inconsistent with new rules. Always version guidelines and note which items were labeled under which version.

Optimizing for Speed Over Quality

Paying per-label incentivizes speed. Use hourly rates or per-label with quality bonuses to ensure annotators prioritize accuracy over throughput.

Quick-Start Checklist

Before You Start

[ ] Data type and volume confirmed

[ ] Label taxonomy finalized and reviewed by ML engineer

[ ] Gold standard set created by domain expert

[ ] Annotation guidelines with examples written

[ ] Edge case decision rules documented

[ ] Budget approved with 20% contingency

During the Project

[ ] Pilot batch (50 items) completed and reviewed

[ ] IAA above minimum threshold

[ ] 10% batch audits on schedule

[ ] Edge cases logged and guidelines updated

[ ] Annotator performance tracked against gold set

[ ] Final QA and delivery sign-off