Prompt — User Summarize
Tiny SAMPLE prompt fixture (user-summarize) for harness and eval wiring.
Summarize the following SAMPLE text in 3 bullets:
Novus Examples ships synthetic fixtures.
Specifications
- Wave
- I
- Role
- prompt-fixture
Testing contract
Expected to pass- Scenario
- Exercise Prompt — User Summarize in its prompts workflow. Tiny SAMPLE prompt fixture (user-summarize) for harness and eval wiring.
- Expected result
- 3 text lines, decoded as UTF-8; first nonempty line is 'Summarize the following SAMPLE text in 3 bullets:'. Declared feature checks: role=prompt-fixture.
What is a .txt file?
TXT is a plain-text file containing unformatted character data with no styling or structure beyond line breaks. Its interpretation depends on character encoding, most commonly UTF-8, and on line-ending convention. It is the most universal and portable text container.
How to use this file
Use an example TXT to test encoding detection, line-ending (LF versus CRLF) handling, and any tool that reads or streams raw text input.
How to use this file for testing
“Prompt — User Summarize” is a deterministic Novus Examples fixture for Prompt engineering, ML training data. A templated prompt library in JSON Lines with tasks, tags, and placeholders, for testing prompt-management tools and JSONL parsers.
Documented properties for this file: prompt-fixture. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
AI/ML fixtures are fully synthetic with documented schemas, no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.
Related files
- jsonlOCR Assistant Chat Turns (JSONL)A single multi-turn chat example for an OCR assistant — system/user/assistant roles in JSONL.
- jsonlPrompt Library (JSONL)A reusable prompt library in JSON Lines — 20 templated prompts for summarization, translation, extraction, code, and more, each with a task label, tags, and {curly-brace} placeholders. A fixture for prompt-management tools and JSONL parsers.
- jsonlASR Digit Utterances Dataset (JSONL)JSON Lines ASR training/eval set for the Wave B synthetic digit utterances — each row points at a clean WAV and carries the expected transcript.
- jsonlChat Fine-tuning Dataset — Anthropic Format (JSONL)The same synthetic conversations in the Anthropic Messages JSONL shape — a top-level system prompt plus a messages array of user and assistant turns. The format twin of the OpenAI file, for testing chat-format conversion.
- jsonlChat Fine-tuning Dataset — OpenAI Format (JSONL)A chat fine-tuning dataset in the OpenAI JSONL format — one conversation per line as a messages array with system, user, and assistant turns. Synthetic Q&A content. Paired with an Anthropic-format twin for testing format converters.
- jsonlClassification Dataset — Hierarchical (JSONL)Hierarchical category labels for taxonomy-aware classifiers.
Generated by generation/pad_wave_i.py. Free for any use, no attribution required, license.