--- title: "Awesome-Prompt-Engineering vs PPASR" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/promptslab-awesome-prompt-engineering-vs-yeyupiaoling-ppasr" tools: ["promptslab-awesome-prompt-engineering", "yeyupiaoling-ppasr"] --- # Awesome-Prompt-Engineering vs PPASR *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-Prompt-Engineering when awesome-Prompt-Engineering is primarily TypeScript; PPASR is Python; pick PPASR when pPASR is primarily Python; Awesome-Prompt-Engineering is TypeScript. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) reports 6.2k GitHub stars, 723 forks, and 88 open issues, last pushed Jul 11, 2026. [PPASR](https://github.com/yeyupiaoling/PPASR) has 873 stars, 129 forks, and 1 open issues, last pushed Dec 17, 2025. Figures are from public GitHub metadata via [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering) and [PPASR's repository](https://github.com/yeyupiaoling/PPASR). | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [PPASR](/tools/yeyupiaoling-ppasr.md) | | --- | --- | --- | | Tagline | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | 基于PaddlePaddle实现端到端中文语音识别，从入门到实战，超简单的入门案例，超实用的企业项目。支持当前最流行的DeepSpeech2、Conformer、Squeezeformer模型 | | Stars | 6,150 | 873 | | Forks | 723 | 129 | | Open issues | 88 | 1 | | Language | TypeScript | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training, Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [PPASR](/tools/yeyupiaoling-ppasr.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 205d | | Open issues (now) | 88 | 1 | | Owner type | Organization | User | | Security scan | No lockfile | 6 low (6 low) | | Full report | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | [trust report](/tools/yeyupiaoling-ppasr/trust.md) | ## Choose when ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; PPASR is Python. - Tags unique to Awesome-Prompt-Engineering: gpt-3, chatgpt-api, few-shot-learning, machine-learning. - Also covers LLM Frameworks, Model Training. ### Choose PPASR if… - PPASR is primarily Python; Awesome-Prompt-Engineering is TypeScript. - Tags unique to PPASR: asr, chinese, speech, conformer. - Leaner open-issue backlog (1). ## When NOT to use Awesome-Prompt-Engineering - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use PPASR - Last GitHub push was 206 days ago (slowing maintenance, Dec 17, 2025). Validate activity before betting a new project on PPASR. ## Common questions ### What is the difference between Awesome-Prompt-Engineering and PPASR? Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc. PPASR: 基于PaddlePaddle实现端到端中文语音识别，从入门到实战，超简单的入门案例，超实用的企业项目。支持当前最流行的DeepSpeech2、Conformer、Squeezeformer模型. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Prompt-Engineering over PPASR? Choose Awesome-Prompt-Engineering over PPASR when Awesome-Prompt-Engineering is primarily TypeScript; PPASR is Python; Tags unique to Awesome-Prompt-Engineering: gpt-3, chatgpt-api, few-shot-learning, machine-learning; Also covers LLM Frameworks, Model Training. ### When should I choose PPASR over Awesome-Prompt-Engineering? Choose PPASR over Awesome-Prompt-Engineering when PPASR is primarily Python; Awesome-Prompt-Engineering is TypeScript; Tags unique to PPASR: asr, chinese, speech, conformer; Leaner open-issue backlog (1). ### When should I avoid Awesome-Prompt-Engineering? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid PPASR? Last GitHub push was 206 days ago (slowing maintenance, Dec 17, 2025). Validate activity before betting a new project on PPASR. ### Is Awesome-Prompt-Engineering or PPASR more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,150 vs 873). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Prompt-Engineering and PPASR open source? Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, PPASR: Apache-2.0). ### Where can I find alternatives to Awesome-Prompt-Engineering or PPASR? GraphCanon lists graph-backed alternatives at [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) and [PPASR alternatives](/tools/yeyupiaoling-ppasr/alternatives) ([Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/alternatives.md), [PPASR markdown twin](/tools/yeyupiaoling-ppasr/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/promptslab-awesome-prompt-engineering-vs-yeyupiaoling-ppasr.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Prompt-Engineering or PPASR? Awesome-Prompt-Engineering: Very active. PPASR: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Awesome-Prompt-Engineering and PPASR? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust); [PPASR trust report](/tools/yeyupiaoling-ppasr/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering`](/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_