Design scenarios
Translate a narrow policy into families, boundaries and missing-information cases.
Practical AI · EDITION 1.0.0
Create Useful Synthetic Evaluation Datasets
Construct a small policy-grounded evaluation dataset with clear provenance, boundary cases, group-preserving splits and reviewable labels, without pretending synthetic examples represent real traffic.
By Sanjaay Gowda T S · For developers and evaluation leads building early ai test suites.
You need: Understand JSON arrays and objects; Read Python conditionals; Know the difference between development and holdout data
Runtime and hardware details
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Understand JSON arrays and objects; Read Python conditionals; Know the difference between development and holdout data.
Full setup and scope ↓$2.99USD · one-time purchase
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review remains pending. Automated checks and agent review are not certification.
PDF + EPUB + companion project. Any applicable tax is shown at checkout. Single-reader ebook terms; companion code is MIT licensed.
TRY THE FREE SAMPLE
See how the guide teaches.
Read 6 sample pages to explore the introduction, first chapter and teaching approach before you choose the complete edition.
Read free sample PDF · No checkout required
WHAT YOU WILL LEARN
Understand each part.
Inspect every boundary.
Protect evaluation
Keep related variants together and preserve dataset provenance.
Review labels
Audit ambiguity and coverage before drawing conclusions from a score.
INSIDE THE BOOK
9 focused chapters.
- Choose the decision the dataset tests
- Build a scenario coverage matrix
- Create families before paraphrases
- Derive labels from explicit policy
- Split by family and freeze provenance
- Audit duplicates and hidden shortcuts
- Review ambiguity and disagreement
- Use the dataset without overclaiming
- Maintain a dataset release
Your download includes
- 22-page tagged PDF with linked contents
- Reflowable EPUB for ebook readers
- Local Python project, fictional input, recorded expected JSON output and 11 companion tests
- 3 explanatory diagrams
- 9 chapters with exercises and worked answers
Before you begin
Python 3.10 or later and a terminal. The supplied project runs locally and uses the Python standard library.
No model, API key, cloud account or network calls are used by the companion.
Learning prerequisites: Understand JSON arrays and objects; Read Python conditionals; Know the difference between development and holdout data.
Prepared with AI assistance. Independent human technical, editorial and assistive-technology reader review is pending.
The included examples are synthetic teaching material. Model access, software subscriptions, hosting, ongoing updates and consulting are not included.
CLEAR EXPECTATIONS
Before you buy.
What will I build?
Construct a small policy-grounded evaluation dataset with clear provenance, boundary cases, group-preserving splits and reviewable labels, without pretending synthetic examples represent real traffic. The local companion uses fictional teaching fixtures. Project limits: Handwritten fictional parcel scenarios; no real customers or generated model outputs. Exact normalized duplicate detection does not detect semantic near-duplicates. Hash partitions are stable, not balanced or representative; small releases require split coverage review.
What was tested?
The 11 companion tests passed on the packaged project. The supplied fictional fixture produced its recorded expected JSON output. EPUBCheck reported no errors or warnings; DAISY Ace and PDF/UA automated checks passed. These local teaching checks are not production accuracy claims or certification. Independent human technical, editorial and assistive-technology reader review remains pending.
How do I get the files?
Complete the secure Lemon Squeezy checkout to access the PDF, EPUB and companion ZIP. Keep your receipt and download access private. This is a one-time purchase of edition 1.0.0; ongoing updates and consulting are not included.
Can I share the book or the companion code?
The full ebook is licensed to one reader. The labelled free sample may be shared unchanged. The companion project is MIT licensed and may be adapted or shared with its licence notice. See the exact ebook terms.
Design scenario families, reliable labels, and honest coverage claims
One book. A complete learning project. Evidence you can inspect.
AI-assisted · Independent human review pending
$2.99USDBuy ebook