Best of RAG — December 2025

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   Article

   freeCodeCamp·23w

   Learn n8n to Design, Develop, and Deploy Production-Grade AI Agents

   n8n is an open-source visual workflow automation tool for connecting applications, APIs, and AI models. A comprehensive beginner course covers building practical AI agents including email automation, research workflows with OpenAI and Perplexity, and a customer support RAG agent using vector databases like Pinecone. The training includes advanced topics like modular component patterns, multi-workflow builds for coordinating agent teams, and deployment options including cloud, Docker, and self-hosting with local LLMs like Ollama.

   170

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2. 2
   Article

   Daily Dose of Data Science | Avi Chawla | Substack·21w

   The AI Engineering Guidebook

   A comprehensive 350+ page guidebook covering the engineering fundamentals of LLM systems, including model architecture, training, prompt engineering, RAG systems, fine-tuning techniques like LoRA, AI agents, Model Context Protocol, optimization strategies, and deployment considerations. The resource focuses on practical engineering decisions, system design tradeoffs, and real-world implementation patterns rather than surface-level usage.

   125

   5
3. 3
   Article

   Weaviate·24w

   Context Engineering for AI Agents

   Context engineering is the discipline of designing systems that feed LLMs the right information at the right time, addressing the fundamental constraint of finite context windows. It encompasses six interdependent pillars: agents that orchestrate decisions, query augmentation that refines user input, retrieval that connects to external knowledge, prompting that guides reasoning, memory that preserves history, and tools that enable real-world action. Unlike prompt engineering which focuses on phrasing instructions, context engineering builds the architecture around the model, treating the context window as a scarce resource and designing retrieval, memory systems, and tool integrations to maximize signal while avoiding context poisoning, distraction, confusion, and clash.

   56
4. 4
   Article

   Keshav Ashiya·23w

   Docify: Building a Production RAG System for Knowledge Management

   Docify is an open-source RAG system that processes documents locally while maintaining AI capabilities. The architecture uses 11 specialized services including async embedding generation with Celery, hybrid search combining pgvector and BM25, multi-factor ranking with citation verification, and token-aware context assembly. Built with PostgreSQL pgvector for vector storage, Redis for task queuing, and Ollama for local LLM inference, it supports heterogeneous document formats and implements deduplication via SHA-256 hashing. The system uses HNSW indexing for sub-200ms vector search, reciprocal rank fusion for search result merging, and citation verification to reduce hallucinations.

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