Define the decision
Separate mandatory release gates from preferences among eligible candidates.
Practical AI · EDITION 1.0.0
Build AI Evaluation Scorecards That Explain Tradeoffs
Build a transparent scorecard from synthetic paired runs, keep critical failures visible, report slice performance and inspect cost-quality-latency tradeoffs without hiding them in one rank.
By Sanjaay Gowda T S · For ai product engineers and evaluation owners choosing release candidates.
You need: Read JSON and simple Python; Calculate a mean and a percentage; Understand a test case and expected outcome
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.
Read JSON and simple Python; Calculate a mean and a percentage; Understand a test case and expected outcome.
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.
Compare fairly
Require paired case coverage and retain per-case and slice evidence.
Explain tradeoffs
Use Pareto comparisons and a decision record with explicit limitations.
INSIDE THE BOOK
9 focused chapters.
- Begin with a release decision
- Define rubric dimensions and anchors
- Pair candidates on the same cases
- Calculate metrics with denominators
- Put gates before preferences
- Expose quality, latency and cost tradeoffs
- Read slices and uncertain evidence
- Review a complete fictional scorecard
- Version the scorecard and decision record
Your download includes
- 21-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: Read JSON and simple Python; Calculate a mean and a percentage; Understand a test case and expected outcome.
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?
Build a transparent scorecard from synthetic paired runs, keep critical failures visible, report slice performance and inspect cost-quality-latency tradeoffs without hiding them in one rank. The local companion uses fictional teaching fixtures. Project limits: All candidate names, quality scores, durations and costs are synthetic fixtures. Two cases per candidate illustrate arithmetic and do not establish statistical superiority. Pareto comparisons use six-decimal displayed means; real applications should preserve sufficient precision and use uncertainty-aware decisions. No model calls, automated judging, bootstrap interval or production release recommendation.
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.
Combine paired evidence, release gates, and readable decision records
One book. A complete learning project. Evidence you can inspect.
AI-assisted · Independent human review pending
$2.99USDBuy ebook