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dair-ai/Prompt-Engineering-Guide

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Latest metrics (snapshot 2026-10-14)

stars79169
forks8695
issues287
pushed_at2026-03-11T20:09:13Z

History

History chart: 1 daily snapshot so far (archive started 2026-10-14). The line fills in as the daily archive grows; full series via the API.

DateGitHub stars
2026-10-1479169

ADC Momentum: 49/100

ADC Momentum 49 Leaderboard

Growth velocity80
Consistency64
Recency0
Agent demand0
Basisbootstrap

ADC Momentum (0-100) = 45% growth velocity (stars/likes gained per day in our daily archive, relative and absolute, percentile-ranked within the category) + 20% consistency (share of recent days with growth) + 20% recency (latest push/update/release, decaying) + 15% agent demand (how often AI agents looked it up via our API/MCP this week). MCP servers use release cadence; LLMs use price cuts, context and recency. Recomputed daily; scores marked "bootstrap" use lifetime rates until the archive has enough days. Method

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Details

description🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
urlhttps://github.com/dair-ai/Prompt-Engineering-Guide
languageMDX
licenseMIT
topicsagent
agents
ai-agents
chatgpt
deep-learning
generative-ai
language-model
llms
openai
prompt-engineering
rag
created_at2022-12-16T16:04:50Z
homepagehttps://www.promptingguide.ai/

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