RAG.md
RAG architecture, retrieval rules, and chunking strategy
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6 docsRecent RAG.md Documents
View allAIP-C01 Exam Concepts: Ranked by Importance
Ranks AIP-C01 exam concepts by importance using official domain weightings and cross-domain relevance.
🤖 Agentic Finance Director — Agent Inventory (Batch 3: AGT-101 → AGT-150)
Catalogues 50 finance AI agents across 5 modules with purpose, triggers, tools, KPIs, and error handling for each.
SUMMARY
Proposes three on-prem AI architectures, modular, hybrid, and fully local RAG, with hardware specs and vendor lists.
Glossar
Defines retrieval, chunking, embedding, search, reranking, synthesis, and evaluation concepts for RAG experiments.
Metrics
Documents evaluation metrics for RAG systems, covering retrieval and generation quality, plus guidance on building golden datasets.
Project Memory
Documents a production-ready RAG system for Paris cultural events with hybrid search, LLM generation, and Streamlit UI.
RAG Deep Dive Part 7: Evaluation and Debugging RAG Systems
Teaches you to measure and debug a RAG pipeline with retrieval metrics, generation metrics, and production monitoring, all implemented from scratch in Python.
Code Span Semantic Chunking Executive Summary
Recommends an AST-driven chunking strategy for code retrieval, preserving function/class boundaries to improve precision and reduce token waste.