ai-rag-and-agents
Skill1 starsChanged 6 months agoMITView on GitHub
Practical knowledge for building RAG (Retrieval-Augmented Generation) systems and AI agents. Covers RAG architecture and retrieval algorithms (term-based, embedding-based, hybrid), retrieval optimization (chunking, reranking, query rewriting), multimodal RAG, agent design with tools and planning, agent failure modes, and memory systems.
In this repository21 other files it gives its agents.
- ai-context-engineeringSkill
- ai-context-engine-productionSkill
- ai-context-engineSkill
- ai-dataset-engineeringSkill
- ai-evaluationSkill
- ai-finetuningSkill
- ai-foundation-modelsSkill
- ai-inference-optimizationSkill
- ai-multi-agent-mcpSkill
- ai-production-architectureSkill
- ai-prompt-engineeringSkill
- ai-rag-defenseSkill
- ddia-architectureSkill
- ddia-batch-stream-processingSkill
- ddia-data-ethicsSkill
- ddia-data-modelingSkill
- ddia-replication-shardingSkill
- ddia-transactions-consistencySkill
- job-description-analyzerSkill
- resume-content-writerSkill
- resume-fit-scorerSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 21
Check guidelines.md to find which files to load for your task
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
AI RAG and Agents
Knowledge from "AI Engineering" by Chip Huyen (Chapter 6). Practical guidance for building retrieval-augmented and agentic systems.
Quick Start
- Check
guidelines.mdto find which files to load for your task - Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
- Apply guidance to your work
Contents
References
| Category | Purpose |
rag-architecture | RAG overview, architecture, when to use RAG |
retrieval-algorithms | Term-based (BM25, TF-IDF), embedding-based, hybrid retrieval |
retrieval-optimization | Chunking strategies, reranking, query rewriting, contextual retrieval, multimodal RAG, tabular RAG |
agent-overview | Agent definition, tools (knowledge augmentation, capability extension, write actions) |
agent-planning | Plan generation, reflection, error correction, tool selection |
agent-failures | Planning failures, tool failures, efficiency issues, evaluation |
agent-memory | Memory systems for agents and conversations |
Workflows
| Task | Workflow |
| Build a RAG system end-to-end | workflows/build-rag.md |
| Build an agent end-to-end | workflows/build-agent.md |
| Diagnose and fix agent failures | workflows/debug-agent.md |
Guidelines
See guidelines.md for task-based file selection.
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