RAG Guides - Master Document AI
Comprehensive guides for implementing RAG (Retrieval-Augmented Generation) systems. Learn to build AI chatbots connected to your documents.
The 6 steps of the RAG pipeline
- Preparation: Document cleaning and conversion
- Chunking: Splitting into optimal segments
- Embedding: Semantic vectorization of texts
- Indexing: Storage in a vector database
- Retrieval: Retrieval of relevant passages
- Generation: LLM response creation
All our RAG guides
AI Customer Support: Reducing Tickets with RAG
Automate your customer support with RAG: reduce up to 70% of tier-1 tickets while improving customer satisfaction.
Reading time: 16 min read
RAG Security and Compliance: GDPR, AI Act, and Best Practices
Complete guide to securing your RAG system: GDPR compliance, European AI Act, sensitive data management, and security auditing.
Reading time: 24 min read
Intelligent Knowledge Base: Centralizing Enterprise Knowledge
Create an AI knowledge base for your company: technical documentation, onboarding, and business expertise accessible instantly.
Reading time: 19 min read
E-commerce AI Chatbot: Boost Conversions with RAG
Deploy an AI chatbot on your online store to increase sales, reduce cart abandonment, and improve customer experience.
Reading time: 22 min read
Best Embedding Models 2025: MTEB Scores & Leaderboard (Cohere, OpenAI, BGE)
Compare MTEB scores for top embedding models: Cohere embed-v4 (65.2), OpenAI text-3-large (64.6), BGE-M3 (63.0). Full leaderboard with pricing.
Reading time: 11 min read
Retrieval Fundamentals: How RAG Search Works
Master the basics of retrieval in RAG systems: embeddings, vector search, chunking, and indexing for relevant results.
Reading time: 18 min read
RAG Generation: Choosing and Optimizing Your LLM
Complete guide to selecting and configuring your LLM in a RAG system: prompting, temperature, tokens, and response optimization.
Reading time: 20 min read
Guardrails for RAG: Securing Your AI Assistants
Implement robust guardrails to prevent dangerous, off-topic, or inappropriate responses in your production RAG systems.
Reading time: 12 min read
Hallucination Detection in RAG Systems
Hallucinations are RAG's Achilles heel. Learn how to detect, measure, and prevent them with proven techniques.
Reading time: 13 min read
Hierarchical Chunking: Preserving Document Structure
Hierarchical chunking maintains parent-child relationships in your documents. Learn how to implement this advanced technique to improve RAG retrieval quality.
Reading time: 11 min read
LLM Reranking: Using LLMs to Reorder Your Results
LLMs can rerank search results with deep contextual understanding. Learn when and how to use this expensive but powerful technique.
Reading time: 10 min read
Advanced E-commerce RAG: Beyond Customer Support
Advanced RAG strategies for e-commerce: personalized recommendations, AI personal shopper, conversational search, and purchase journey optimization.
Reading time: 12 min read
RAG + Google Drive: Create a Chatbot on Your Business Documents
Connect Google Drive to an AI assistant to query your documents in natural language. Complete guide to deploying a RAG chatbot on your document base.
Reading time: 8 min read
Healthcare RAG: AI Assistant for the Medical Sector
Deploy an AI assistant in healthcare: patient information, medical team support, and protocol valorization. Guide with regulatory considerations.
Reading time: 11 min read
RAG for HR: Onboarding and Internal Knowledge Base
Deploy an AI assistant for your HR teams: automated onboarding, employee question answering, and internal documentation valorization.
Reading time: 10 min read
Legal RAG: Automating Document Analysis with AI
Discover how RAG transforms the legal sector: case law research, contract analysis, and attorney assistance. Complete guide with use cases.
Reading time: 12 min read
AI Chatbot for Beauty Salons: Automate Customer Responses
Integrate an AI assistant for your beauty salon to automatically answer customer questions about services, prices, and availability. Works with any booking system.
Reading time: 7 min read
AI Chatbot for PrestaShop: RAG Integration Guide
Deploy an intelligent AI assistant on your PrestaShop store. Automate customer support, recommend products, and boost conversions with RAG technology.
Reading time: 9 min read
Real Estate RAG: AI Assistant for Agencies and Property Managers
Deploy a RAG chatbot for real estate: tenant question answering, property portfolio management, and technical documentation valorization.
Reading time: 10 min read
AI Chatbot for Shopify: Complete RAG Integration Guide
Learn how to deploy an intelligent chatbot on your Shopify store using RAG technology. Automated customer support, product recommendations, and increased conversions.
Reading time: 10 min read
AI Chatbot for WooCommerce: RAG Integration on WordPress
Complete guide to deploying an intelligent AI assistant on your WooCommerce store. Automate customer support and boost sales with RAG technology.
Reading time: 10 min read
Table Extraction and Processing for RAG
Tables contain critical structured data but are difficult to parse. Master table extraction and chunking techniques for RAG.
Reading time: 11 min read
Fixed-Size Chunking: Fast and Reliable
Master the basics: implement fixed-size chunking with overlaps for consistent, predictable RAG performance.
Reading time: 7 min read
OCR for Scanned Documents and Images
Extract text from scanned PDFs and images for RAG. Compare Tesseract, AWS Textract, and Google Vision OCR with code examples and accuracy benchmarks.
Reading time: 9 min read
Reduce RAG Latency: From 2000ms to 200ms
10x faster RAG: parallel retrieval, streaming responses, and architectural optimizations for sub-200ms latency.
Reading time: 12 min read
Caching Strategies to Reduce RAG Latency and Cost
Cut costs by 80%: implement semantic caching, embedding caching, and response caching for production RAG.
Reading time: 10 min read
Qdrant: Advanced Vector Search Features
Leverage Qdrant's powerful features: payload indexing, quantization, distributed deployment for high-performance RAG.
Reading time: 13 min read
Multilingual Embeddings for Global RAG
Build RAG systems that work across languages using multilingual embedding models and cross-lingual retrieval.
Reading time: 11 min read
Pinecone for Production RAG at Scale
Deploy production-ready vector search: Pinecone setup, indexing strategies, and scaling to billions of vectors.
Reading time: 12 min read
Cohere Rerank API for Production RAG
Boost RAG accuracy by 40% with Cohere's Rerank API: simple integration, multilingual support, production-ready.
Reading time: 8 min read
Fine-Tune Embeddings for Your Domain
Boost RAG retrieval accuracy by 30-50% with domain-specific fine-tuning. Learn to create custom embeddings for your documents and queries.
Reading time: 14 min read
Cross-Encoder Reranking for RAG Precision
Achieve 95%+ precision: use cross-encoders to rerank retrieved documents and eliminate false positives.
Reading time: 11 min read
Weaviate: GraphQL-Powered Vector Database
Complete Weaviate setup guide for RAG: Docker deployment, GraphQL queries, hybrid search, multi-tenancy, and built-in generative modules.
Reading time: 12 min read
Milvus: Billion-Scale Vector Search
Deploy Milvus for production-scale RAG handling billions of vectors with horizontal scaling and GPU acceleration.
Reading time: 13 min read
MMR: Diversify Search Results with Maximal Marginal Relevance
Reduce redundancy in RAG retrieval: use MMR to balance relevance and diversity for better context quality.
Reading time: 9 min read
Hybrid Search for RAG: BM25 + Vector Search Tutorial (2025)
Boost RAG retrieval accuracy by 20-30% with hybrid search. Step-by-step tutorial combining BM25 keyword matching with vector search using Weaviate, Qdrant, or Pinecone.
Reading time: 10 min read
Query Expansion: Retrieve More Relevant Results
Improve recall by 40%: expand user queries with synonyms, sub-queries, and LLM-generated variations.
Reading time: 10 min read
Parent Document Retrieval: Context Without Noise
Search small chunks, retrieve full documents: the best of both precision and context for RAG systems.
Reading time: 9 min read
ChromaDB Setup for RAG Applications
Get started with ChromaDB: lightweight, fast vector database perfect for prototyping and production RAG systems.
Reading time: 9 min read
RAG Cost Optimization: Cut Spending by 90%
Reduce RAG costs from $10k to $1k/month: smart chunking, caching, model selection, and batch processing.
Reading time: 11 min read
RAG Monitoring and Observability
Monitor RAG systems in production: track latency, costs, accuracy, and user satisfaction with metrics and dashboards.
Reading time: 12 min read
Getting Started with RAG: Core Components
Build your first RAG system step by step. Understand embeddings, vector databases, and retrieval to create AI assistants connected to your data.
Reading time: 8 min
Semantic Chunking for Better Retrieval
Split documents intelligently based on meaning, not just length. Learn semantic chunking techniques for RAG.
Reading time: 12 min read
Parse PDF Documents with PyMuPDF
Master PDF parsing: extract text, images, tables, and metadata from PDFs using PyMuPDF and alternatives.
Reading time: 10 min read
Document Parsing Fundamentals
Start your RAG journey: learn how to extract text, metadata, and structure from documents for semantic search.
Reading time: 8 min read
Context Window Optimization: Managing Token Limits
Strategies for fitting more information in limited context windows: compression, summarization, smart selection, and window management techniques.
Reading time: 11 min read
Query Optimization: Making Retrieval More Effective
Techniques to optimize user queries for better retrieval: query rewriting, expansion, decomposition, and routing strategies.
Reading time: 10 min read
Deploying RAG Systems to Production
Production-ready RAG: architecture, scaling, monitoring, error handling, and operational best practices for reliable deployments.
Reading time: 14 min read
Evaluating RAG Systems: Metrics and Methodologies
Comprehensive guide to measuring RAG performance: retrieval metrics, generation quality, end-to-end evaluation, and automated testing frameworks.
Reading time: 12 min read
Reranking for RAG: +40% Accuracy with Cross-Encoders (2025 Guide)
Boost RAG accuracy by 40% using reranking. Complete guide to cross-encoders, Cohere Rerank API, and ColBERT for production retrieval systems.
Reading time: 11 min read
Advanced Retrieval Strategies for RAG
Beyond basic similarity search: hybrid search, query expansion, MMR, and multi-stage retrieval for better RAG performance.
Reading time: 13 min read
Best Vector Databases for RAG in 2025: Pinecone vs Qdrant vs Weaviate
Complete comparison of vector databases for RAG: Pinecone, Qdrant, Weaviate, Milvus, Chroma. Benchmarks, pricing, and recommendations for your use case.
Reading time: 14 min read
Agentic RAG: Building AI Agents with Dynamic Knowledge Retrieval
Comprehensive guide to Agentic RAG: architecture, design patterns, implementing autonomous agents with knowledge retrieval, multi-tool orchestration, and advanced use cases.
Reading time: 25 min read
Best RAG Platforms in 2025: Complete Comparison Guide
Compare the best RAG platforms and RAG-as-a-Service solutions in 2025. Detailed analysis of features, pricing, and use cases to help you choose the right platform.
Reading time: 12 min read
RAG Chunking Strategies 2025: Optimal Chunk Sizes & Techniques
Master document chunking for RAG: optimal chunk sizes (512-1024 tokens), overlap strategies, semantic vs fixed-size splitting. Improve retrieval by 25%+.
Reading time: 15 min read
How to Build a RAG Chatbot: Complete Step-by-Step Tutorial
Learn how to build a production-ready RAG chatbot from scratch. This complete tutorial covers document processing, embeddings, vector storage, retrieval, and deployment.
Reading time: 20 min read
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.
Reading time: 12 min read
RAG as a Service: The Complete Guide to Production RAG Platforms
Learn what RAG as a Service (RAG-as-a-Service) is, why it's the fastest way to deploy production RAG applications, and how to choose the right platform for your needs.
Reading time: 15 min read
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.
Reading time: 12 min read
Why follow our RAG guides?
- Practical approach with code examples
- Based on production projects
- Regularly updated with latest advances
- Covers fundamentals and advanced techniques