Research Papers And Findings Collection On Prompt Engineering Rag And Agents

via “context engineering and prompt optimization for agent behavior”

📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程

Unique: Treats context engineering as a first-class capability with explicit patterns for system messages, role definitions, and output format constraints, providing concrete examples of how prompt structure influences agent behavior across different paradigms (ReAct, Plan-and-Solve, Reflection)

vs others: More practical and immediate than fine-tuning for behavior modification, but less systematic than formal reinforcement learning; enables rapid iteration on agent behavior without retraining

via “research paper indexing and agentic rag paper collection”

https://adongwanai.github.io/AgentGuide | AI Agent开发指南 | LangGraph实战 | 高级RAG | 转行大模型 | 大模型面试 | 算法工程师 | 面试题库 | 强化学习｜数据合成

Unique: Separates agentic RAG papers from general agent papers, reflecting the emergence of agentic RAG as a distinct research area; provides context on paper relevance to practical development

vs others: Curated for agent development relevance rather than comprehensive; includes emerging agentic RAG research that general paper collections may not prioritize

via “prompt-engineering-technique-aggregation”

A curated list of Generative AI tools, works, models, and references

Unique: Treats prompt engineering as a first-class capability with dedicated resources and subcategories, rather than burying it within LLM documentation. Recognizes that prompt design is a critical skill for LLM application development, separate from model selection or fine-tuning

vs others: More comprehensive than single-model documentation (OpenAI's prompt engineering guide) by covering techniques across multiple models, but less interactive than specialized platforms (Prompt.com, PromptBase) which provide prompt marketplaces and community sharing

via “research papers and findings collection on prompt engineering, rag, and agents”

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

Unique: Integrates research papers within a practical guide, bridging the gap between academic research and practitioner knowledge by providing both theoretical foundations and practical applications

vs others: More curated than raw paper databases because papers are selected and summarized; more accessible than academic conferences because summaries distill key findings; more current than textbooks because it includes recent research

via “advanced-prompt-engineering-technique-documentation”

Curated list of chatgpt prompts from the top-rated GPTs in the GPTs Store. Prompt Engineering, prompt attack & prompt protect. Advanced Prompt Engineering papers.

Unique: Curates a focused collection of peer-reviewed papers specifically on advanced prompting techniques (CoT, ToT, GoT, SoT, AoT) organized by technique type, serving as a bridge between academic research and practical prompt engineering rather than a general LLM research repository.

vs others: Provides a curated, technique-focused research index that's more accessible than searching arXiv or Google Scholar, while remaining more rigorous and research-grounded than generic prompt engineering blogs or tutorials.

via “curated-prompt-engineering-research-indexing”

This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

Unique: Provides hand-curated, topic-organized research index specifically focused on prompt engineering rather than general LLM research, with explicit categorization by technique (reasoning methods, evaluation, applications) rather than chronological or venue-based sorting

vs others: More targeted than general ML paper repositories (arXiv, Papers with Code) because it filters specifically for prompt engineering relevance and organizes by practical technique rather than requiring keyword search

via “structured prompt engineering for agent reasoning”

Ralph TUI - AI Agent Loop Orchestrator

Unique: Implements structured prompt composition specifically for agent loops, with sections for tool definitions, execution history, and decision instructions, rather than generic prompt templates

vs others: More specialized for agent reasoning than generic prompt engineering libraries, with built-in support for tool context and execution history management

via “rag-context-window-and-prompt-engineering-guide”

A curated list of tools and resources for building production RAG systems.

Unique: Focuses on prompt engineering specific to RAG systems where context is retrieved dynamically, addressing challenges like handling irrelevant context and managing variable context lengths vs static prompt optimization

vs others: More RAG-specific than generic prompt engineering guides, addressing retrieval-specific challenges (handling irrelevant or conflicting documents, variable context lengths) vs general LLM prompt optimization

via “prompt engineering research paper collection and synthesis”

Guide and resources for prompt engineering.

via “agent prompt engineering and instruction design”

A book about building AI agents with tools, memory, planning, and multi-agent systems.

Unique: Treats prompt engineering as a systematic discipline with patterns for role definition, constraint encoding, and output formatting rather than ad-hoc trial-and-error

vs others: More agent-focused than generic prompt engineering guides because it addresses multi-step reasoning, tool use, and error recovery in prompts

via “research paper collection and citation management for prompt engineering”

via “research-paper-synthesis”