--- title: "Prompt-Engineering-Guide vs awesome-notebookLM-prompts" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dair-ai-prompt-engineering-guide-vs-serenakeyitan-awesome-notebooklm-prompts" tools: ["dair-ai-prompt-engineering-guide", "serenakeyitan-awesome-notebooklm-prompts"] --- # Prompt-Engineering-Guide vs awesome-notebookLM-prompts *GraphCanon updated Jul 11, 2026* ## Verdict Pick Prompt-Engineering-Guide when tags unique to Prompt-Engineering-Guide: agent, agents, chatgpt, deep-learning; pick awesome-notebookLM-prompts when tags unique to awesome-notebookLM-prompts: ai, gemini, google, nanobananapro. [Prompt-Engineering-Guide](https://www.promptingguide.ai/) reports 76k GitHub stars, 8.4k forks, and 274 open issues, last pushed Mar 11, 2026. [awesome-notebookLM-prompts](https://github.com/serenakeyitan/awesome-notebookLM-prompts) has 4.1k stars, 584 forks, and 1 open issues, last pushed Jun 19, 2026. Figures are from public GitHub metadata via [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide) and [awesome-notebookLM-prompts's repository](https://github.com/serenakeyitan/awesome-notebookLM-prompts). | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [awesome-notebookLM-prompts](/tools/serenakeyitan-awesome-notebooklm-prompts.md) | | --- | --- | --- | | Tagline | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | A curated collection of the strongest NotebookLM slide prompts sourced from the real creative underground . Your go-to resource for AI powerpoint :P | | Stars | 76,349 | 4,111 | | Forks | 8,361 | 584 | | Open issues | 274 | 1 | | Language | MDX | - | | Adopt for | Decision-critical facts for Prompt-Engineering-Guide | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [awesome-notebookLM-prompts](/tools/serenakeyitan-awesome-notebooklm-prompts.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 121d | 22d | | Open issues (now) | 274 | 1 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/dair-ai-prompt-engineering-guide/trust.md) | [trust report](/tools/serenakeyitan-awesome-notebooklm-prompts/trust.md) | ## Decision facts: Prompt-Engineering-Guide - **Adopt for:** Decision-critical facts for Prompt-Engineering-Guide ## Choose when ### Choose Prompt-Engineering-Guide if… - Tags unique to Prompt-Engineering-Guide: agent, agents, chatgpt, deep-learning. - When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. - More GitHub stars (76k vs 4.1k) - visibility, not fit. ### Choose awesome-notebookLM-prompts if… - Tags unique to awesome-notebookLM-prompts: ai, gemini, google, nanobananapro. - More recently updated (last pushed Jun 19, 2026). ## 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 awesome-notebookLM-prompts - 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. ## Common questions ### What is the difference between Prompt-Engineering-Guide and awesome-notebookLM-prompts? Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. awesome-notebookLM-prompts: A curated collection of the strongest NotebookLM slide prompts sourced from the real creative underground . Your go-to resource for AI powerpoint :P. See the comparison table for live GitHub stats and shared categories. ### When should I choose Prompt-Engineering-Guide over awesome-notebookLM-prompts? Choose Prompt-Engineering-Guide over awesome-notebookLM-prompts when Tags unique to Prompt-Engineering-Guide: agent, agents, chatgpt, deep-learning; When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques; More GitHub stars (76k vs 4.1k) - visibility, not fit. ### When should I choose awesome-notebookLM-prompts over Prompt-Engineering-Guide? Choose awesome-notebookLM-prompts over Prompt-Engineering-Guide when Tags unique to awesome-notebookLM-prompts: ai, gemini, google, nanobananapro; More recently updated (last pushed Jun 19, 2026). ### 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 awesome-notebookLM-prompts? 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. ### Is Prompt-Engineering-Guide or awesome-notebookLM-prompts more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 4,111). Stars measure visibility, not whether either tool fits your constraints. ### Are Prompt-Engineering-Guide and awesome-notebookLM-prompts open source? Yes - both are open-source projects on GitHub (Prompt-Engineering-Guide: MIT, awesome-notebookLM-prompts: MIT). ### Where can I find alternatives to Prompt-Engineering-Guide or awesome-notebookLM-prompts? GraphCanon lists graph-backed alternatives at [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) and [awesome-notebookLM-prompts alternatives](/tools/serenakeyitan-awesome-notebooklm-prompts/alternatives) ([Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/alternatives.md), [awesome-notebookLM-prompts markdown twin](/tools/serenakeyitan-awesome-notebooklm-prompts/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-serenakeyitan-awesome-notebooklm-prompts.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 awesome-notebookLM-prompts? Prompt-Engineering-Guide: Slowing. awesome-notebookLM-prompts: Active. 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 awesome-notebookLM-prompts? 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); [awesome-notebookLM-prompts trust report](/tools/serenakeyitan-awesome-notebooklm-prompts/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/_