--- title: "Awesome-Prompt-Engineering vs P-tuning-v2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/promptslab-awesome-prompt-engineering-vs-thudm-p-tuning-v2" tools: ["promptslab-awesome-prompt-engineering", "thudm-p-tuning-v2"] --- # Awesome-Prompt-Engineering vs P-tuning-v2 *GraphCanon updated Aug 3, 2026* ## Verdict Pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license; pick P-tuning-v2 if p-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) reports 6.2k GitHub stars, 734 forks, and 94 open issues, last pushed Jul 27, 2026. [P-tuning-v2](https://github.com/THUDM/P-tuning-v2) has 2.1k stars, 213 forks, and 35 open issues, last pushed Nov 16, 2023. Figures are from public GitHub metadata via [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering) and [P-tuning-v2's repository](https://github.com/THUDM/P-tuning-v2). | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Tagline | Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers | Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks | | Stars | 6,197 | 2,077 | | Forks | 734 | 213 | | Open issues | 94 | 35 | | Language | TypeScript | Python | | Adopt for | Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. | P-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | P-tuning-v2 is provided under the Apache-2.0 license which permits free use, modification and distribution as long as copyright and license notice are preserved. | | Categories | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 990d | | Open issues (now) | 94 | 35 | | Full report | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | [trust report](/tools/thudm-p-tuning-v2/trust.md) | ## Shared compatibility - **Python**: [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) - Python runtime; [P-tuning-v2](/tools/thudm-p-tuning-v2.md) - Python runtime ## Decision facts: Awesome-Prompt-Engineering - **Adopt for:** Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. ## Decision facts: P-tuning-v2 - **Requirements:** Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance. - **Adopt for:** P-tuning-v2 is an optimized deep prompt tuning strategy that can be used for tasks like natural language processing and parameter-efficient learning where few parameters need to be adjusted compared to full fine-tuning. - **License detail:** P-tuning-v2 is provided under the Apache-2.0 license which permits free use, modification and distribution as long as copyright and license notice are preserved. ## Choose when ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; P-tuning-v2 is Python. - Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt. - Also covers Developer Tools. - You need focused materials on GPT and related models for prompt engineering ### Choose P-tuning-v2 if… - P-tuning-v2 is primarily Python; Awesome-Prompt-Engineering is TypeScript. - Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance.. - Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model. - For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods. ## When NOT to use Awesome-Prompt-Engineering - The project requires languages other than TypeScript - Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ## When NOT to use P-tuning-v2 - Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods. - If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity. ## Common questions ### What is the difference between Awesome-Prompt-Engineering and P-tuning-v2? Awesome-Prompt-Engineering: Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers. P-tuning-v2: Optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Prompt-Engineering over P-tuning-v2? Choose Awesome-Prompt-Engineering over P-tuning-v2 when Awesome-Prompt-Engineering is primarily TypeScript; P-tuning-v2 is Python; Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt; Also covers Developer Tools; You need focused materials on GPT and related models for prompt engineering. ### When should I choose P-tuning-v2 over Awesome-Prompt-Engineering? Choose P-tuning-v2 over Awesome-Prompt-Engineering when P-tuning-v2 is primarily Python; Awesome-Prompt-Engineering is TypeScript; Requirements: Min 8 GB RAM; Experiments were conducted on NVIDIA DGX-A100; however, RTX 3090 or RTX 2080 Ti GPUs can suffice given certain conditions.; CUDA version should be at least 11.1 and specific versions of pytorch (1.7.1), torchvision (0.8.2) and torchaudio (0.7.2).; A conda environment setup with Python 3.8.5 is recommended for optimal performance.; Tags unique to P-tuning-v2: natural-language-processing, p-tuning, parameter-efficient-learning, pretrained-language-model; For scenarios requiring efficient use of resources, as P-tuning v2 requires fewer parameters than traditional fine-tuning methods. ### When should I avoid Awesome-Prompt-Engineering? The project requires languages other than TypeScript Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ### When should I avoid P-tuning-v2? Avoid using P-tuning v2 when you have large-scale datasets and model architectures, as it might not be as effective compared to full fine-tuning methods. If your project requires a high level of customization beyond prompt tuning or continuous prompts do not align with the problem complexity. ### Is Awesome-Prompt-Engineering or P-tuning-v2 more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,197 vs 2,077). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Prompt-Engineering and P-tuning-v2 open source? Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, P-tuning-v2: Apache-2.0). ### Where can I find alternatives to Awesome-Prompt-Engineering or P-tuning-v2? GraphCanon lists graph-backed alternatives at [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) and [P-tuning-v2 alternatives](/tools/thudm-p-tuning-v2/alternatives) ([Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/alternatives.md), [P-tuning-v2 markdown twin](/tools/thudm-p-tuning-v2/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-thudm-p-tuning-v2.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 P-tuning-v2? Awesome-Prompt-Engineering: Very active. P-tuning-v2: Dormant. 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 P-tuning-v2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust); [P-tuning-v2 trust report](/tools/thudm-p-tuning-v2/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/_