{"data":{"slug":"promptslab-awesome-prompt-engineering","name":"Awesome-Prompt-Engineering","tagline":"Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers","github_url":"https://github.com/promptslab/Awesome-Prompt-Engineering","owner":"promptslab","repo":"Awesome-Prompt-Engineering","owner_avatar_url":"https://avatars.githubusercontent.com/u/120981762?v=4","primary_language":"TypeScript","stars":6197,"forks":734,"topics":["chatgpt","chatgpt-api","deep-learning","few-shot-learning","gpt","gpt-3","machine-learning","openai","prompt","prompt-based-learning","prompt-engineering","prompt-generator","prompt-learning","prompt-toolkit","prompt-tuning","promptengineering","text-to-image","text-to-speech","text-to-video"],"archived":false,"github_pushed_at":"2026-07-27T10:03:26+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/promptslab-awesome-prompt-engineering","markdown_url":"https://www.graphcanon.com/tools/promptslab-awesome-prompt-engineering.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/promptslab-awesome-prompt-engineering","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=promptslab-awesome-prompt-engineering","description":"This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc ","homepage_url":"https://discord.gg/m88xfYMbK6","license":"Apache-2.0","open_issues":94,"watchers":93,"ai_summary":"Provides comprehensive materials related to prompt engineering including GPT, ChatGPT, and PaLM models.","readme_excerpt":"### Platform Ports & Hardware Forks\n\n- [gianfrancopiana/openclaw-autoresearch](https://github.com/gianfrancopiana/openclaw-autoresearch) — OpenClaw port of pi-autoresearch; autonomous experiment loop for any optimization target with statistical confidence scoring.\n- [miolini/autoresearch-macos](https://github.com/miolini/autoresearch-macos) — Widely adopted macOS fork that adapts upstream autoresearch for Apple Silicon / MPS while preserving the original loop shape.\n- [trevin-creator/autoresearch-mlx](https://github.com/trevin-creator/autoresearch-mlx) — MLX-native Apple Silicon port that keeps the upstream fixed-budget `val_bpb` loop while removing the PyTorch/CUDA dependency entirely.\n- [jsegov/autoresearch-win-rtx](https://github.com/jsegov/autoresearch-win-rtx) — Windows-native RTX fork focused on consumer NVIDIA GPUs, with explicit VRAM floors and a practical desktop setup path.\n- [iii-hq/n-autoresearch](https://github.com/iii-hq/n-autoresearch) — Multi-GPU autoresearch infrastructure with structured experiment tracking, adaptive search strategy, crash recovery, and queryable orchestration around the classic `train.py` loop.\n- [lucasgelfond/autoresearch-webgpu](https://github.com/lucasgelfond/autoresearch-webgpu) — Browser/WebGPU port that lets agents generate training code, run experiments in-browser, and feed results back into the loop without a Python setup.\n- [tonitangpotato/autoresearch-engram](https://github.com/tonitangpotato/autoresearch-engram) — Fork with **persistent cognitive memory** — frequency-weighted retrieval of cross-session knowledge for improved experiment continuity.\n- [Colab/Kaggle T4 port](https://github.com/karpathy/autoresearch/issues/208) — Adapts autoresearch for free T4 GPUs (Google Colab / Kaggle) with zero cost and zero local setup. Key changes: Flash Attention 3 → PyTorch SDPA, removes H100-only kernel dependency.\n- [ArmanJR-Lab/autoautoresearch](https://github.com/ArmanJR-Lab/autoautoresearch) — Jetson AGX Orin port with a **director** — a Go binary that acts as a \"creative director\" injecting novelty (arxiv papers + DeepSeek Reasoner) into the loop to escape local minima. Includes multi-experiment comparison (baseline vs director-guided) with detailed stall analysis.","github_created_at":"2023-02-09T18:22:52+00:00","created_at":"2026-07-11T11:57:13.757199+00:00","updated_at":"2026-07-28T00:00:47.712012+00:00","categories":[{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"chatgpt","name":"chatgpt"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"few-shot-learning","name":"few-shot-learning"},{"slug":"gpt","name":"gpt"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"openai","name":"openai"},{"slug":"prompt-engineering","name":"prompt-engineering"},{"slug":"prompt-tuning","name":"prompt-tuning"}],"trust":{"provenance":{"is_fork":false,"github_id":599716050,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-28T00:00:46.564Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":0,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:57:15.123Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-28T00:00:47.020Z"},"languages":{"value":["typescript"],"source":"github.language","observed_at":"2026-07-28T00:00:47.020Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-07-28T00:00:47.020Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["You need focused materials on GPT and related models for prompt engineering","Your project uses or plans to use TypeScript","Apache-2.0 licensing is preferred for your resource"],"when_not_to_use":["The project requires languages other than TypeScript","Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering"],"source":"enrich:decision_facts","observed_at":"2026-07-16T23:12:13.266Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license."}]}}