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RAG Implementation Guide

Step-by-step technical guides for building production RAG systems

Learn every component of the RAG pipeline, from document parsing to optimization. Each guide provides practical implementation details and code examples.

The RAG Pipeline

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Click on any step to jump to its guides

1

Parsing

3 guides

Document parsing and extraction

Start Here

Document Parsing Fundamentals

Start your RAG journey: learn how to extract text, metadata, and structure from documents for semantic search.

parsingdocument processingtext extraction

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GuideIntermediate

OCR for Scanned Documents and Images

Extract text from scanned PDFs and images using Tesseract, AWS Textract, and modern OCR techniques.

ocrparsingtesseract

GuideIntermediate

Parse PDF Documents with PyMuPDF

Master PDF parsing: extract text, images, tables, and metadata from PDFs using PyMuPDF and alternatives.

pdfparsingpymupdf

2

Chunking

3 guides

Text splitting strategies

Start Here

GuideIntermediate

Chunking Strategies: Optimizing Document Segmentation

Master document chunking techniques to improve retrieval quality. Learn about chunk sizes, overlaps, semantic splitting, and advanced strategies.

chunkingdocument processingretrieval

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Fixed-Size Chunking: Fast and Reliable

Master the basics: implement fixed-size chunking with overlaps for consistent, predictable RAG performance.

chunkingfixed-sizesimple

Semantic Chunking for Better Retrieval

Split documents intelligently based on meaning, not just length. Learn semantic chunking techniques for RAG.

chunkingsemanticnlp

3

Embedding

4 guides

Vector embedding models

Start Here

GuideIntermediate

Embeddings: The Foundation of Semantic Search

Deep dive into embedding models, vector representations, and how to choose the right embedding strategy for your RAG system.

embeddingsvectorssemantic search

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GuideIntermediate

Multilingual Embeddings for Global RAG

Build RAG systems that work across languages using multilingual embedding models and cross-lingual retrieval.

embeddingmultilingualcross-lingual

Fine-Tune Embeddings for Your Domain

Boost retrieval accuracy by 30%: fine-tune embedding models on your specific documents and queries.

embeddingfine-tuningcustom

GuideIntermediate

Choosing Embedding Models for RAG

Compare embedding models in 2025: OpenAI, Cohere, open-source alternatives. Find the best fit for your use case.

embeddingsmodelsbenchmarks

4

Storage

6 guides

Vector database solutions

Start Here

GuideIntermediate

Vector Databases: Storing and Searching Embeddings

Comprehensive guide to vector databases for RAG: comparison of popular options, indexing strategies, and performance optimization.

vector databaseindexingsimilarity search

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Qdrant: Advanced Vector Search Features

Leverage Qdrant's powerful features: payload indexing, quantization, distributed deployment for high-performance RAG.

qdrantvector databaseperformance

GuideIntermediate

Pinecone for Production RAG at Scale

Deploy production-ready vector search: Pinecone setup, indexing strategies, and scaling to billions of vectors.

pineconevector databaseproduction

GuideIntermediate

Weaviate: GraphQL-Powered Vector Database

Set up Weaviate for production RAG with GraphQL queries, hybrid search, and generative modules.

weaviatevector databasegraphql

Milvus: Billion-Scale Vector Search

Deploy Milvus for production-scale RAG handling billions of vectors with horizontal scaling and GPU acceleration.

milvusvector databasescale

ChromaDB Setup for RAG Applications

Get started with ChromaDB: lightweight, fast vector database perfect for prototyping and production RAG systems.

chromadbvector databasesetup

5

Retrieval

5 guides

Search and retrieval techniques

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MMR: Diversify Search Results with Maximal Marginal Relevance

Reduce redundancy in RAG retrieval: use MMR to balance relevance and diversity for better context quality.

mmrretrievaldiversity

GuideIntermediate

Hybrid Search: Combine Semantic and Keyword Search

Boost retrieval accuracy by 20-30%: combine vector search with BM25 keyword matching for superior RAG performance.

hybrid searchbm25retrieval

GuideIntermediate

Query Expansion: Retrieve More Relevant Results

Improve recall by 40%: expand user queries with synonyms, sub-queries, and LLM-generated variations.

retrievalquery expansionrecall

GuideIntermediate

Parent Document Retrieval: Context Without Noise

Search small chunks, retrieve full documents: the best of both precision and context for RAG systems.

retrievalchunkingcontext

Advanced Retrieval Strategies for RAG

Beyond basic similarity search: hybrid search, query expansion, MMR, and multi-stage retrieval for better RAG performance.

retrievalhybrid searchquery expansion

6

Reranking

3 guides

Result reranking methods

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Reranking: Improving Retrieval Precision

Cross-encoders, LLM-based reranking, and reranking strategies to optimize retrieved context for better RAG responses.

rerankingcross-encoderretrieval

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GuideIntermediate

Cohere Rerank API for Production RAG

Boost RAG accuracy by 40% with Cohere's Rerank API: simple integration, multilingual support, production-ready.

coherererankapi

Cross-Encoder Reranking for RAG Precision

Achieve 95%+ precision: use cross-encoders to rerank retrieved documents and eliminate false positives.

rerankingcross-encoderprecision

7

Optimization

5 guides

Performance and tuning

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Context Window Optimization: Managing Token Limits

Strategies for fitting more information in limited context windows: compression, summarization, smart selection, and window management techniques.

context windowtokensoptimization

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Reduce RAG Latency: From 2000ms to 200ms

10x faster RAG: parallel retrieval, streaming responses, and architectural optimizations for sub-200ms latency.

latencyoptimizationperformance

GuideIntermediate

Caching Strategies to Reduce RAG Latency and Cost

Cut costs by 80%: implement semantic caching, embedding caching, and response caching for production RAG.

cachingoptimizationcost

GuideIntermediate

RAG Cost Optimization: Cut Spending by 90%

Reduce RAG costs from $10k to $1k/month: smart chunking, caching, model selection, and batch processing.

optimizationcostbudget

RAG Monitoring and Observability

Monitor RAG systems in production: track latency, costs, accuracy, and user satisfaction with metrics and dashboards.

optimizationmonitoringobservability

General Guides

Additional resources and comprehensive guides

Getting Started with RAG: Core Components

Learn how to build your first RAG system by understanding and assembling the essential components

beginnerembeddingsvector-database

GuideIntermediate

Query Optimization: Making Retrieval More Effective

Techniques to optimize user queries for better retrieval: query rewriting, expansion, decomposition, and routing strategies.

query optimizationretrievalperformance

Deploying RAG Systems to Production

Production-ready RAG: architecture, scaling, monitoring, error handling, and operational best practices for reliable deployments.

productiondeploymentscaling

Evaluating RAG Systems: Metrics and Methodologies

Comprehensive guide to measuring RAG performance: retrieval metrics, generation quality, end-to-end evaluation, and automated testing frameworks.

evaluationmetricstesting

Introduction to Retrieval-Augmented Generation (RAG)

Understanding the fundamentals of RAG systems: what they are, why they matter, and how they combine retrieval and generation for better AI responses.

RAGfundamentalsarchitecture