--- title: "Awesome-Prompt-Engineering vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/promptslab-awesome-prompt-engineering-vs-rasbt-llms-from-scratch" tools: ["promptslab-awesome-prompt-engineering", "rasbt-llms-from-scratch"] --- # Awesome-Prompt-Engineering vs LLMs-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick Awesome-Prompt-Engineering when awesome-Prompt-Engineering is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; 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. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 99k stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 6,150 | 98,899 | | Forks | 723 | 15,183 | | Open issues | 88 | 4 | | Language | TypeScript | Jupyter Notebook | | Adopt for | - | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | LLM Frameworks, Model Training, Speech & Audio | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 38d | | Open issues (now) | 88 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | ## Decision facts: LLMs-from-scratch - **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. ## Choose when ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook. - License: Awesome-Prompt-Engineering is Apache-2.0, LLMs-from-scratch is Other. - Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, few-shot-learning, gpt-3. - Also covers Speech & Audio. ### Choose LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; Awesome-Prompt-Engineering is TypeScript. - License: LLMs-from-scratch is Other, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention mechanism, finetuning. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## 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 LLMs-from-scratch - - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 - a deeper learning experience. ## Common questions ### What is the difference between Awesome-Prompt-Engineering and LLMs-from-scratch? 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. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Prompt-Engineering over LLMs-from-scratch? Choose Awesome-Prompt-Engineering over LLMs-from-scratch when Awesome-Prompt-Engineering is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook; License: Awesome-Prompt-Engineering is Apache-2.0, LLMs-from-scratch is Other; Tags unique to Awesome-Prompt-Engineering: chatgpt, chatgpt-api, few-shot-learning, gpt-3; Also covers Speech & Audio. ### When should I choose LLMs-from-scratch over Awesome-Prompt-Engineering? Choose LLMs-from-scratch over Awesome-Prompt-Engineering when LLMs-from-scratch is primarily Jupyter Notebook; Awesome-Prompt-Engineering is TypeScript; License: LLMs-from-scratch is Other, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention mechanism, finetuning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### 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 LLMs-from-scratch? - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience. ### Is Awesome-Prompt-Engineering or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 6,150). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Prompt-Engineering and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (Awesome-Prompt-Engineering: Apache-2.0, LLMs-from-scratch: Other). ### Where can I find alternatives to Awesome-Prompt-Engineering or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/alternatives.md), [LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/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-rasbt-llms-from-scratch.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 LLMs-from-scratch? Awesome-Prompt-Engineering: Very active. LLMs-from-scratch: Steady. 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 LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/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/_