Comparison
Awesome-Prompt-Engineering vs LLMs-from-scratch
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.
Markdown twin · Awesome-Prompt-Engineering alternatives · LLMs-from-scratch alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | Awesome-Prompt-Engineering | LLMs-from-scratch |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1d · github_public_v1 | Steady (38d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Personal account As of 1d · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | No lockfile As of 1d · none |
Tagline
- 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
Stars
- Awesome-Prompt-Engineering
- 6.2k
- LLMs-from-scratch
- 99k
Forks
- Awesome-Prompt-Engineering
- 723
- LLMs-from-scratch
- 15k
Open issues
- Awesome-Prompt-Engineering
- 88
- LLMs-from-scratch
- 4
Language
- Awesome-Prompt-Engineering
- TypeScript
- LLMs-from-scratch
- Jupyter Notebook
Adopt for
- Awesome-Prompt-Engineering
- -
- LLMs-from-scratch
- 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
- Awesome-Prompt-Engineering
- -
- LLMs-from-scratch
- -
Runtime
- Awesome-Prompt-Engineering
- -
- LLMs-from-scratch
- -
License
- Awesome-Prompt-Engineering
- Apache-2.0
- LLMs-from-scratch
- Other
Last pushed
- Awesome-Prompt-Engineering
- Jul 11, 2026
- LLMs-from-scratch
- Jun 2, 2026
Categories
- Awesome-Prompt-Engineering
- LLM Frameworks, Model Training, Speech & Audio
- LLMs-from-scratch
- LLM Frameworks, Model Training
Trust and health
Maintenance
- Awesome-Prompt-Engineering
- Very active (96%)
- LLMs-from-scratch
- Steady (60%)
Days since push
- Awesome-Prompt-Engineering
- 0d
- LLMs-from-scratch
- 38d
Open issues (now)
- Awesome-Prompt-Engineering
- 88
- LLMs-from-scratch
- 4
Owner type
- Awesome-Prompt-Engineering
- Organization
- LLMs-from-scratch
- User
Full report
- Awesome-Prompt-Engineering
- Trust report
- LLMs-from-scratch
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (promptslab/Awesome-Prompt-Engineering) · observed Jul 11, 2026
- GitHub forks (promptslab/Awesome-Prompt-Engineering) · observed Jul 11, 2026
- Last push (promptslab/Awesome-Prompt-Engineering) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (rasbt/LLMs-from-scratch) · observed Jul 11, 2026
- GitHub forks (rasbt/LLMs-from-scratch) · observed Jul 11, 2026
- Last push (rasbt/LLMs-from-scratch) · observed Jun 2, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Prompt-Engineering 6.2k · LLMs-from-scratch 99k (synced Jul 11, 2026).
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 and LLMs-from-scratch alternatives (Awesome-Prompt-Engineering markdown twin, LLMs-from-scratch markdown twin), 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 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; LLMs-from-scratch trust report.