--- title: "Prompt-Engineering-Guide vs Made-With-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dair-ai-prompt-engineering-guide-vs-gokumohandas-made-with-ml" tools: ["dair-ai-prompt-engineering-guide", "gokumohandas-made-with-ml"] --- # Prompt-Engineering-Guide vs Made-With-ML *GraphCanon updated Jul 15, 2026* ## Verdict Pick Prompt-Engineering-Guide when prompt-Engineering-Guide is primarily MDX; Made-With-ML is Jupyter Notebook; pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; Prompt-Engineering-Guide is MDX. [Prompt-Engineering-Guide](https://www.promptingguide.ai/) reports 76k GitHub stars, 8.4k forks, and 274 open issues, last pushed Mar 11, 2026. [Made-With-ML](https://madewithml.com) has 49k stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. Figures are from public GitHub metadata via [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide) and [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML). | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Tagline | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | Learn how to develop, deploy and iterate on production-grade ML applications. | | Stars | 76,349 | 48,703 | | Forks | 8,361 | 7,661 | | Open issues | 274 | 27 | | Language | MDX | Jupyter Notebook | | Adopt for | Decision-critical facts for Prompt-Engineering-Guide | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Days since push | 121d | 132d | | Open issues (now) | 274 | 27 | | Owner type | Organization | User | | Full report | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | ## Decision facts: Prompt-Engineering-Guide - **Adopt for:** Decision-critical facts for Prompt-Engineering-Guide ## Choose when ### Choose Prompt-Engineering-Guide if… - Prompt-Engineering-Guide is primarily MDX; Made-With-ML is Jupyter Notebook. - Tags unique to Prompt-Engineering-Guide: agent, agents, ai-agents, chatgpt. - When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; Prompt-Engineering-Guide is MDX. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml. - Also covers Model Training. ## When NOT to use Prompt-Engineering-Guide - Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. - Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ## When NOT to use Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - 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. ## Common questions ### What is the difference between Prompt-Engineering-Guide and Made-With-ML? Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Prompt-Engineering-Guide over Made-With-ML? Choose Prompt-Engineering-Guide over Made-With-ML when Prompt-Engineering-Guide is primarily MDX; Made-With-ML is Jupyter Notebook; Tags unique to Prompt-Engineering-Guide: agent, agents, ai-agents, chatgpt; When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. ### When should I choose Made-With-ML over Prompt-Engineering-Guide? Choose Made-With-ML over Prompt-Engineering-Guide when Made-With-ML is primarily Jupyter Notebook; Prompt-Engineering-Guide is MDX; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml; Also covers Model Training. ### When should I avoid Prompt-Engineering-Guide? Avoid using if your focus is entirely on deep-learning frameworks without a need for detailed instructions or examples related to prompt crafting. Not suitable when you require tools that go beyond guiding materials, such as custom prompts or direct software plugins provided by competitors focused more on practical implementation over learning. ### When should I avoid Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. 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. ### Is Prompt-Engineering-Guide or Made-With-ML more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 48,703). Stars measure visibility, not whether either tool fits your constraints. ### Are Prompt-Engineering-Guide and Made-With-ML open source? Yes - both are open-source projects on GitHub (Prompt-Engineering-Guide: MIT, Made-With-ML: MIT). ### Where can I find alternatives to Prompt-Engineering-Guide or Made-With-ML? GraphCanon lists graph-backed alternatives at [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) and [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) ([Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/alternatives.md), [Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/dair-ai-prompt-engineering-guide-vs-gokumohandas-made-with-ml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Prompt-Engineering-Guide or Made-With-ML? Prompt-Engineering-Guide: Slowing. Made-With-ML: 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 Prompt-Engineering-Guide and Made-With-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Prompt-Engineering-Guide trust report](/tools/dair-ai-prompt-engineering-guide/trust); [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dair-ai-prompt-engineering-guide`](/api/graphcanon/graph?tool=dair-ai-prompt-engineering-guide) - 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/_