Ebook for AI builders

No Claim Without Evidence

How to Build AI Systems You Can Verify

A field guide for building LLM workflows where every important output can be traced, tested, reviewed, or blocked before it reaches a user.

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One-time purchase · PDF + EPUB

No Claim Without Evidence cover

What you get

  • PDF and EPUB editions
  • Secure Dodo checkout and file delivery
  • One-time purchase with no subscription

The thesis

Clean AI output is not the same thing as a trustworthy system.

The book starts from a small airline-ticket extraction failure: the source document does not show a terminal, but the model confidently returns one. From there, it builds the operating discipline needed for serious AI products.

Evidence records for LLM outputs
Extraction vs inference vs normalization
Eval sets, scorecards, and error taxonomies
Review workflows and release gates
Observability for AI product decisions
Agent action evals and pipeline tests

Curated reading sample

Judge the writing, examples, and operating method before you buy.

This is a real web-formatted reading sample, not a synopsis. It brings together selected excerpts from the introduction, three chapters, and a reusable template so you can evaluate the book across field-level evidence, pipeline diagnosis, and release decisions.

Inside the sample

Five substantial selections, properly formatted for reading.

  1. 01Introduction: the evidence habit
  2. 02Chapter 1: the unsupported airline-ticket fields
  3. 03Chapter 15: testing the pipeline instead of blaming the model
  4. 04Chapter 19: turning evals into a release gate
  5. 05A reusable claim-evidence ledger

Operating method

Evidence, pipeline evaluation, and practical artifacts.

Evidence before confidence

Treat every AI output as a claim. Accept it only when the system can show source evidence, status, and the action taken.

Evaluate the pipeline

The model call is only one layer. The book covers prompts, schemas, fallback rules, review policy, data/config, and release gates.

Use practical artifacts

Includes field-record patterns, eval manifests, error taxonomy examples, and an airline-ticket extraction case study.

Who it is for

Builders who need AI systems to survive contact with real users.

Written for solo founders, AI engineers, product-minded developers, PMs, and operators building extraction, review, automation, or agent workflows. It is deliberately practical: short chapters, concrete examples, and reusable patterns.

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One-time purchase · PDF + EPUB

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For consulting

Need help shaping the same kind of workflow for your team?

The enquiry path is for custom work, workshops, or scoped advisory. Use it when you want a human review of your workflow, not just the ebook.

  • Workflow review and scoping
  • Implementation help for AI extraction or review systems
  • Team workshops and productized advisory
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Purchase and delivery

A standard digital product, separate from custom work.

  • One-time regional price with no recurring subscription.
  • Secure PDF and EPUB access is delivered after successful payment.
  • Dodo Payments handles payment, applicable tax, receipt, and secure file delivery.
  • Consulting and implementation services require a separate written scope.