Below are 10 actionable steps to collect, curate, and evaluate training data for an AI system — with specific tools you can use (including no-code / low-code options where possible).

1️⃣ Define the Task & Data Schema

Action

• Clearly define: input → output → constraints → edge cases.

• Create a structured JSON schema for training examples.

• Define evaluation criteria before collecting data.

Tools

• Notion (spec documentation)

• Confluence

• Google Sheets

• JSON Schema

2️⃣ Identify & Inventory Data Sources

Action

• List internal + external sources.

• Classify: structured, semi-structured, unstructured.

• Check licensing and compliance.

Tools

• Airtable (source inventory tracking)

• Miro (data mapping)

• OneTrust (compliance tracking)

3️⃣ Collect Structured Data

Action

• Export from databases or SaaS tools.

• Normalize formats (CSV/JSON).

• Log extraction date & version.

Tools

• Fivetran

• Zapier (no-code pipelines)

• Make

• Snowflake

4️⃣ Collect Unstructured Data (Docs, PDFs, Web)

Action

• Crawl relevant documents.

• Convert to clean text.

• Deduplicate.

Tools

• Apify (no-code scraping)

• Diffbot

• Unstructured

• Beautiful Soup (if coding allowed)

5️⃣ Clean & Normalize Data

Action

• Remove duplicates.

• Standardize formats.

• Strip PII if needed.

• Fix encoding issues.

Tools

• OpenRefine (excellent no-code)

• Talend

• Trifacta

6️⃣ Annotate / Label Data

Action

• Create annotation guidelines.

• Use multiple reviewers.

• Track inter-annotator agreement.

Tools

• Labelbox

• Prodigy

• Scale AI

• Amazon SageMaker Ground Truth

7️⃣ Create Evaluation & Test Sets

Action

• Hold out 10–20% of data.

• Include adversarial / edge cases.

• Build golden dataset manually reviewed by SMEs.

Tools

• Weights & Biases

• Arize AI

• Comet

8️⃣ Build an Evaluation Harness (Automated Scoring)

Action

• Define metrics (accuracy, BLEU, F1, hallucination rate).

• Automate scoring pipeline.

• Log regression across versions.

Tools

• LangChain evaluation modules

• LangSmith

• Ragas

• TruEra

9️⃣ Human-in-the-Loop Review

Action

• Review model outputs weekly.

• Tag failure modes.

• Update training data accordingly.

Tools

• Humanloop

• Google Forms

• Slack feedback workflows

🔟 Version, Govern & Monitor Drift

Action

• Version datasets.

• Track schema changes.

• Monitor production drift.

• Archive deprecated samples.

Tools

• DVC

• LakeFS

• Databricks

• Monte Carlo

Outputs

• • A standard JSON schema

  • A golden evaluation dataset

  • A repeatable scoring harness