TakaGoto/rag-learning-academy

TakaGoto/rag-learning-academy

Agent Claude Code

Guides RAG system design decisions, component integration, trade-off analysis, and architectural patterns for building robust retrieval-augmented generation systems.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches document splitting strategies including fixed, recursive, semantic, and agentic chunking, overlap optimization, and chunk size tuning for optimal RAG performance.

TakaGoto/rag-learning-academy

Agent Claude Code

Oversees the RAG learning path, tracks learner progression, detects knowledge gaps, and orchestrates the overall learning experience across all agents.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches production RAG deployment including caching strategies, scaling patterns, monitoring, cost optimization, latency reduction, and operational best practices.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches PDF, HTML, and markdown parsing, table extraction, OCR, multimodal document handling, and data cleaning strategies for RAG ingestion pipelines.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches embedding models, vector space concepts, similarity metrics, dimensionality reduction, and model selection for RAG applications.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches RAG evaluation frameworks, metrics design, quality gates, and systematic approaches to measuring and improving RAG system performance.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches hands-on RAGAS implementation, custom metric design, A/B testing for RAG systems, regression detection, and continuous evaluation workflows.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches knowledge graph integration with RAG, entity extraction, graph construction, GraphRAG patterns, and structured knowledge retrieval techniques.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches BM25 + dense retrieval fusion, reciprocal rank fusion, sparse embeddings (SPLADE), and hybrid search pipeline design for comprehensive retrieval.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches vector database architecture, indexing algorithms (HNSW, IVF, PQ), storage optimization, and the internals of how vector search actually works.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches end-to-end RAG pipeline construction, framework selection (LangChain vs LlamaIndex vs custom), deployment strategies, and connecting all RAG components into working systems.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches metadata extraction, filtering strategies, namespace design, tagging taxonomies, and how to leverage metadata to dramatically improve RAG retrieval quality.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches image, table, and chart RAG, vision embeddings, multimodal retrieval, and techniques for building RAG systems that go beyond text.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches context injection patterns, prompt templates for RAG, few-shot RAG, citation formatting, and the art of instructing LLMs to use retrieved context effectively.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches query understanding, expansion, decomposition, HyDE (Hypothetical Document Embeddings), step-back prompting, and query preprocessing for improved RAG retrieval.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches cross-encoder reranking, ColBERT, Cohere Rerank, and reranking pipeline design for improving retrieval precision in RAG systems.

TakaGoto/rag-learning-academy

Agent Claude Code

Tracks the latest RAG research papers, emerging techniques, benchmark comparisons, and translates academic advances into practical learning content.

TakaGoto/rag-learning-academy

Agent Claude Code

Teaches search strategies including dense, sparse, and hybrid retrieval, ranking algorithms, and retrieval optimization for RAG systems.

TakaGoto/rag-learning-academy

Agent Claude Code

Provides hands-on guidance for working with vector databases including Chroma, Pinecone, Weaviate, pgvector, and Qdrant — setup, migration, querying, and operational best practices.