Advanced RAG: Query Expansion, Reranking, and Fusion
Improve RAG retrieval quality with advanced techniques: query expansion, multi-query retrieval, cross-encoder reranking, and reciprocal rank fusion.
·11 min read · #rag#retrieval#reranking
#rag
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RAG in Production: Build a Reliable Pipeline
Build a production-ready RAG pipeline with document ingestion, chunking, retrieval, and generation. Covers error handling, monitoring, and optimization.
·11 min read · #rag#production#pipeline
Vector Databases: Pinecone vs Weaviate vs Chroma
Compare Pinecone, Weaviate, and Chroma vector databases for RAG applications. Covers setup, performance, pricing, and when to use each one.
·9 min read · #vector-database#pinecone#weaviate
LLM Context Windows: Trade-offs Beyond Token Count
Why bigger context windows are not always better: cost, attention degradation, retrieval design, and how to architect for long-context tasks.
·5 min read · #ai#llm#context-window
LLM Fine-tuning vs Prompting Trade-offs
Decide between prompt engineering, retrieval, and fine-tuning by weighing cost, latency, control, and data requirements honestly.
·4 min read · #llm#fine-tuning#prompting
Grounding vs RAG: What's the Actual Difference?
RAG and grounding are often used interchangeably but they describe different techniques. Here is how to tell them apart and when each one matters.
·4 min read · #llm#rag#grounding
RAG Chunk Overlap Strategies
Learn how chunk overlap rescues boundary context in RAG pipelines, with practical strategies for choosing overlap size and shape for different corpora.
·4 min read · #rag#chunking#overlap
RAG Chunking Strategies Explained
Compare fixed-size, sentence, semantic, and structural chunking for retrieval augmented generation and pick the right one for your corpus.
·5 min read · #rag#chunking#embeddings
RAG Document Loaders Overview
An overview of document loaders in RAG pipelines, covering common formats, libraries, and how to choose the right loader for your data.
·4 min read · #rag#document-loaders#ingestion
RAG Evaluation Metrics Tutorial
Measure RAG quality with recall@k, MRR, context precision, faithfulness, and answer relevancy so you can iterate on data, not vibes.
·5 min read · #rag#evaluation#metrics
Introduction to Graph RAG
Graph RAG combines knowledge graphs with retrieval augmented generation to handle multi-hop questions and complex reasoning.
·4 min read · #rag#graph-rag#knowledge-graph
RAG Hybrid Search: BM25 + Vectors
Combine lexical BM25 with dense vector search to recover the queries each method misses on its own and boost RAG retrieval quality.
·4 min read · #rag#bm25#hybrid-search
RAG HyDE: Hypothetical Document Embeddings
Learn how Hypothetical Document Embeddings (HyDE) improve RAG recall by embedding a generated answer instead of the raw query, with examples and trade-offs.
·4 min read · #rag#hyde#embeddings
RAG Metadata Filtering Strategies
How to use metadata filters in RAG to improve precision, scope retrieval, and enforce permissions without sacrificing recall.
·4 min read · #rag#metadata#retrieval
RAG Reranking Models Overview
Add a reranker on top of vector retrieval to dramatically improve top-k quality with cross-encoders, late interaction, and LLM rerankers.
·4 min read · #rag#reranker#cross-encoder
RAG Self-Query Retrievers Tutorial
Build self-query retrievers that translate natural language into structured metadata filters plus a semantic query for more precise RAG results.
·4 min read · #rag#self-query#retrievers
RAG Tracing with LangSmith Tutorial
Use LangSmith to trace, debug, and evaluate RAG pipelines step by step, from instrumentation to dataset replay and regression detection.
·4 min read · #rag#langsmith#tracing
RAG vs Fine-Tuning: Which One Should You Use?
A practical comparison of RAG and fine-tuning, with guidance on when to choose each, and when to combine them in production systems.
·4 min read · #rag#fine-tuning#llm
RAG Retrieval Strategies
Practical retrieval strategies for RAG: chunking, hybrid search, reranking, query rewriting, metadata filtering, and evaluation patterns that actually move the needle.
·5 min read · #ai#rag#retrieval
Vector Databases Compared
A grounded comparison of vector databases for RAG and semantic search: pgvector, Pinecone, Weaviate, Qdrant, Milvus, and Chroma, with guidance on when each shines.
·5 min read · #ai#vector-database#rag
pgvector: Add Vector Search to Postgres
Use pgvector to run embeddings, similarity search, and hybrid retrieval inside Postgres. Schemas, indexes, and a working Python pipeline.
·4 min read · #pgvector#postgres#intermediate
Pinecone Vector Database: Index, Upsert, Query
Build a working vector search pipeline with Pinecone in Python. Indexes, upserts, metadata filters, hybrid search, and patterns for production RAG.
·4 min read · #pinecone#vector-database#intermediate
Text Embeddings: The Foundation of Semantic Search
What an embedding is, why cosine similarity works, how dimensionality and chunking choices affect retrieval, and a tiny numpy example you can run in your head.
·7 min read · #rag#embeddings#ai
Vector Databases: A Practical Overview
Why a normal database struggles with vector search, how ANN indexes like HNSW and IVF work, and a clear comparison of pgvector, Qdrant, Pinecone, Chroma, and Weaviate so you can pick one.
·8 min read · #rag#vector-db#ai
What Is RAG? Retrieval-Augmented Generation Explained
A clear introduction to retrieval-augmented generation — why LLMs don't know your data, how embeddings and vector search solve it, and when RAG beats fine-tuning.
·9 min read · #rag#llm#beginners