--- title: "Prompt-Engineering-Guide vs best_AI_papers_2022" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dair-ai-prompt-engineering-guide-vs-louisfb01-best-ai-papers-2022" tools: ["dair-ai-prompt-engineering-guide", "louisfb01-best-ai-papers-2022"] --- # Prompt-Engineering-Guide vs best_AI_papers_2022 *GraphCanon updated Jul 11, 2026* ## Verdict Pick Prompt-Engineering-Guide when tags unique to Prompt-Engineering-Guide: agent, agents, ai-agents, chatgpt; pick best_AI_papers_2022 when tags unique to best_AI_papers_2022: 2022, ai, artificial-intelligence, computer-science. [Prompt-Engineering-Guide](https://www.promptingguide.ai/) reports 76k GitHub stars, 8.4k forks, and 274 open issues, last pushed Mar 11, 2026. [best_AI_papers_2022](https://www.louisbouchard.ai) has 3.2k stars, 198 forks, and 0 open issues, last pushed Oct 18, 2023. Figures are from public GitHub metadata via [Prompt-Engineering-Guide's repository](https://github.com/dair-ai/Prompt-Engineering-Guide) and [best_AI_papers_2022's repository](https://github.com/louisfb01/best_AI_papers_2022). | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | | --- | --- | --- | | Tagline | Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents | A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code. | | Stars | 76,349 | 3,188 | | Forks | 8,361 | 198 | | Open issues | 274 | 0 | | 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, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Prompt-Engineering-Guide](/tools/dair-ai-prompt-engineering-guide.md) | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 121d | 997d | | Open issues (now) | 274 | 0 | | 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/louisfb01-best-ai-papers-2022/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, ai-agents, chatgpt. - When you seek comprehensive documentation and educational materials specifically focused on the nuance of prompt engineering techniques. - More GitHub stars (76k vs 3.2k) - visibility, not fit. ### Choose best_AI_papers_2022 if… - Tags unique to best_AI_papers_2022: 2022, ai, artificial-intelligence, computer-science. - Also covers Vector Databases. - Leaner open-issue backlog (0). ## 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 best_AI_papers_2022 - Last GitHub push was 997 days ago (dormant maintenance, Oct 18, 2023). Validate activity before betting a new project on best_AI_papers_2022. - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between Prompt-Engineering-Guide and best_AI_papers_2022? Prompt-Engineering-Guide: Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents. best_AI_papers_2022: A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Prompt-Engineering-Guide over best_AI_papers_2022? Choose Prompt-Engineering-Guide over best_AI_papers_2022 when 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; More GitHub stars (76k vs 3.2k) - visibility, not fit. ### When should I choose best_AI_papers_2022 over Prompt-Engineering-Guide? Choose best_AI_papers_2022 over Prompt-Engineering-Guide when Tags unique to best_AI_papers_2022: 2022, ai, artificial-intelligence, computer-science; Also covers Vector Databases; Leaner open-issue backlog (0). ### 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 best_AI_papers_2022? Last GitHub push was 997 days ago (dormant maintenance, Oct 18, 2023). Validate activity before betting a new project on best_AI_papers_2022. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is Prompt-Engineering-Guide or best_AI_papers_2022 more popular on GitHub? Prompt-Engineering-Guide has more GitHub stars (76,349 vs 3,188). Stars measure visibility, not whether either tool fits your constraints. ### Are Prompt-Engineering-Guide and best_AI_papers_2022 open source? Yes - both are open-source projects on GitHub (Prompt-Engineering-Guide: MIT, best_AI_papers_2022: MIT). ### Where can I find alternatives to Prompt-Engineering-Guide or best_AI_papers_2022? GraphCanon lists graph-backed alternatives at [Prompt-Engineering-Guide alternatives](/tools/dair-ai-prompt-engineering-guide/alternatives) and [best_AI_papers_2022 alternatives](/tools/louisfb01-best-ai-papers-2022/alternatives) ([Prompt-Engineering-Guide markdown twin](/tools/dair-ai-prompt-engineering-guide/alternatives.md), [best_AI_papers_2022 markdown twin](/tools/louisfb01-best-ai-papers-2022/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-louisfb01-best-ai-papers-2022.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 best_AI_papers_2022? Prompt-Engineering-Guide: Slowing. best_AI_papers_2022: 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 Prompt-Engineering-Guide and best_AI_papers_2022? 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); [best_AI_papers_2022 trust report](/tools/louisfb01-best-ai-papers-2022/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/_