References: Structured Prompts and Reliable Outputs

1. JSON - Wikipedia - Offers an accessible overview of JSON, including definitions, methods, examples, limitations, and related concepts. This foundation helps students reason carefully about designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

2. Data validation - Wikipedia - Offers an accessible overview of Data validation, including definitions, methods, examples, limitations, and related concepts. Its examples help students evaluate evidence for designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

3. Data model - Wikipedia - Offers an accessible overview of Data model, including definitions, methods, examples, limitations, and related concepts. It supplies useful context for decisions about designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

4. Designing Data-Intensive Applications - Martin Kleppmann - O'Reilly Media - Explains schemas, data models, reliability, versioning, derived data, and system tradeoffs that illuminate structured-output design. Its sustained treatment supports work on designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

5. Prompt Engineering for Generative AI - James Phoenix and Mike Taylor - O'Reilly Media - Demonstrates reusable prompt patterns, examples, templates, structured responses, chaining, evaluation, and iterative improvement. Its cases illuminate tradeoffs involved in designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

6. Structured Outputs - OpenAI - Shows how schema-constrained generation can produce predictable fields and types while clarifying validation, refusals, supported schema features, and failure handling. Its methods give teams a starting point for designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

7. JSON Schema: Getting Started - JSON Schema - Demonstrates how schemas describe required fields, types, nested structures, arrays, and validation rules for machine-readable structured outputs. Its comparisons clarify choices involved in designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

8. Prompt Engineering Guide - OpenAI - Presents practical techniques for writing clear instructions, supplying relevant context, using examples, structuring tasks, and improving model reliability through iteration. Its framework strengthens responsible work on designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

9. Working with Evals - OpenAI - Introduces test data, evaluation criteria, graders, repeated runs, and comparison workflows for measuring model behavior instead of relying on impressions. Its examples show how evidence informs designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.

10. Demystifying Evals for AI Agents - Anthropic - Explains agent evaluation design, realistic tasks, outcome and process graders, repeated trials, transcript review, and analysis of variable behavior. Its implementation advice helps teams practice designing reusable prompt templates, chains, schemas, evidence fields, and missing-data behavior.