Add Protologue v1.0.0 dataset (101 terms, 10 categories)

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: Protologue
+size_categories:
+- n<1K
+task_categories:
+- text-retrieval
+- question-answering
+tags:
+- prompt-engineering
+- llm
+- glossary
+- taxonomy
+- definitions
+- ai-agents
+- retrieval-augmented-generation
+configs:
+- config_name: terms
+  data_files: data/terms.jsonl
+  default: true
+- config_name: categories
+  data_files: data/categories.jsonl
+---
+
+# Protologue: a taxonomy of prompting and LLM techniques
+
+[Protologue](https://protologue.com) is an openly licensed reference for the vocabulary of prompting and large language model techniques. Each entry has a permanent identifier, a standalone one-to-two-sentence definition, synonyms, broader, narrower and related links, and the primary source that introduced the technique.
+
+This dataset mirrors version 1.0.0 (2026-10-11): **101 techniques** in **10 branches**, citing **98 primary sources**. Every arXiv citation was checked against the paper.
+
+## Configurations
+
+- `terms` (default): one row per technique.
+- `categories`: the 10 branches and the techniques in each.
+
+## Fields (`terms`)
+
+| Field | Description |
+|---|---|
+| `id` | Permanent identifier, e.g. `PTL-0024`. Never reused. |
+| `slug` | URL slug; links in `broader`, `narrower` and `related` use slugs. |
+| `term` | Preferred name. |
+| `aliases` | Synonyms and alternate names. |
+| `category` | Branch of the taxonomy. |
+| `definition` | Standalone definition, quotable on its own. |
+| `description` | Context, limitations and relations. |
+| `example` | Illustrative prompt, where useful. |
+| `broader`, `narrower`, `related` | Links to other entries (SKOS semantics). |
+| `introduced` | Year the technique was introduced. |
+| `sources` | Primary sources: title, authors, year, url. The first is the type source. |
+| `url` | Canonical page on protologue.com. |
+| `citation` | Ready-made citation for the entry. |
+
+## Usage
+
+```python
+from datasets import load_dataset
+terms = load_dataset("franklinpolymathic/protologue", "terms", split="train")
+print(terms[0]["term"], "-", terms[0]["definition"])
+```
+
+The same data is available live as JSON, CSV and SKOS at <https://protologue.com/data/>, and through a free MCP server for AI agents at `https://mcp.protologue.com/mcp` (see <https://protologue.com/mcp/>).
+
+## License and citation
+
+Text and data are licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). When you use a definition, cite the entry's `citation` and link to its `url`. To cite the dataset:
+
+```
+Protologue. (2026). Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0). https://protologue.com/
+```
+
+Corrections and suggestions: open an issue at <https://github.com/protologue-org/protologue>.

data/categories.jsonl

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+{"id":"foundations","name":"Foundations","description":"Core concepts of prompting — the parts of a prompt, how models consume it, and the basic zero-shot and few-shot paradigms.","url":"https://protologue.com/c/foundations/","terms":["Prompt","Prompt Engineering","System Prompt","Context Window","Token","Temperature","Top-p Sampling","Zero-shot Prompting","Few-shot Prompting","Instruction Tuning","Reinforcement Learning from Human Feedback","Direct Preference Optimization","Prompt Template","Delimiters","Prefill","Role Prompting"]}
+{"id":"exemplars","name":"Exemplars & In-Context Learning","description":"How demonstrations inside a prompt are chosen, ordered, scaled, and calibrated, and what they actually teach the model.","url":"https://protologue.com/c/exemplars/","terms":["In-Context Learning","Demonstration Label Sensitivity","Exemplar Selection","Exemplar Ordering","Few-shot Calibration","Many-shot In-Context Learning","Active Prompting"]}
+{"id":"reasoning","name":"Reasoning Elicitation","description":"Techniques that get a model to produce intermediate reasoning, decompose problems, or explore multiple solution paths before answering.","url":"https://protologue.com/c/reasoning/","terms":["Chain-of-Thought Prompting","Zero-shot Chain-of-Thought","Scratchpad","Automatic Chain-of-Thought","Self-Consistency","Universal Self-Consistency","Least-to-Most Prompting","Plan-and-Solve Prompting","Step-Back Prompting","Tree of Thoughts","Graph of Thoughts","Skeleton-of-Thought","Analogical Prompting","Self-Discover","Contrastive Chain-of-Thought","Thread of Thought","System 2 Attention","Rephrase and Respond","Self-Ask","Maieutic Prompting","Generated Knowledge Prompting","Program-Aided Language Models","Program of Thoughts"]}
+{"id":"verification","name":"Self-Critique & Verification","description":"Techniques in which a model, or a set of models, checks, critiques, votes on, or revises outputs.","url":"https://protologue.com/c/verification/","terms":["Self-Refine","Reflexion","Chain-of-Verification","LLM-as-a-Judge","Multi-Agent Debate","Mixture-of-Agents","Process Reward Model","Best-of-N Sampling"]}
+{"id":"retrieval-tools","name":"Retrieval & Tool Use","description":"Grounding generation in external information and letting models call functions, search, and other tools.","url":"https://protologue.com/c/retrieval-tools/","terms":["Retrieval-Augmented Generation","Hypothetical Document Embeddings","Self-RAG","ReAct","Toolformer","Function Calling","Model Context Protocol","Structured Outputs"]}
+{"id":"agents","name":"Agents & Orchestration","description":"Patterns for composing multiple model calls, tools, and memory into workflows and autonomous agents.","url":"https://protologue.com/c/agents/","terms":["AI Agent","Agentic Workflow","Prompt Chaining","Routing","Parallelization","Orchestrator-Workers","Evaluator-Optimizer","Meta-Prompting","Agent Memory","Context Engineering"]}
+{"id":"optimization","name":"Prompt Optimization","description":"Automatic and learned methods that search for, compress, or train better prompts.","url":"https://protologue.com/c/optimization/","terms":["Automatic Prompt Engineer","Optimization by Prompting","DSPy","Prompt Tuning","Prefix Tuning","Low-Rank Adaptation","Directional Stimulus Prompting","Prompt Compression","Prompt Caching","Emotion Prompting"]}
+{"id":"test-time","name":"Reasoning Models & Test-Time Compute","description":"Models trained to reason at length, and methods that improve answers by spending more computation at inference time.","url":"https://protologue.com/c/test-time/","terms":["Reasoning Model","Test-Time Compute Scaling","Extended Thinking","Self-Taught Reasoner"]}
+{"id":"security","name":"Security & Adversarial Prompting","description":"Attacks that subvert a model's instructions and the defenses designed to resist them.","url":"https://protologue.com/c/security/","terms":["Prompt Injection","Indirect Prompt Injection","Prompt Leaking","Jailbreak","Adversarial Suffix","Many-shot Jailbreaking","Instruction Hierarchy","Spotlighting","Constitutional AI"]}
+{"id":"failure-modes","name":"Failure Modes & Evaluation","description":"Systematic ways prompted models go wrong, and the evaluations used to measure them.","url":"https://protologue.com/c/failure-modes/","terms":["Hallucination","Sycophancy","Lost in the Middle","Needle in a Haystack","Prompt Sensitivity","Unfaithful Chain-of-Thought"]}