Best of RAG — June 2025

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Article
Daily Dose of Data Science | Avi Chawla | Substack·50w
9 MCP Projects for AI Engineers
A comprehensive collection of 9 Model Control Protocol (MCP) projects designed for AI engineers, covering various applications from local MCP clients and agentic RAG systems to voice agents and synthetic data generators. The projects demonstrate how to integrate MCP with popular tools like Claude Desktop and Cursor IDE, enabling developers to build more sophisticated AI applications with enhanced tool connectivity and context sharing capabilities.
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12
2
Article
Medium·49w
How to Build Production Ready AI Agents in 5 Steps
A comprehensive 5-step guide for building production-ready AI agents, covering Python foundations with FastAPI and async programming, implementing robust testing and logging, mastering RAG for knowledge retrieval, designing scalable agent architectures with frameworks like LangGraph, and establishing continuous monitoring and improvement processes. The guide emphasizes moving beyond prototype demos to create reliable, maintainable systems that can handle real-world production environments.
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5
3
Article
ByteByteGo·49w
EP167: Top 20 AI Concepts You Should Know
A comprehensive overview of 20 essential AI concepts including machine learning, deep learning, neural networks, NLP, computer vision, and transformers. Also covers the AI application stack for building RAG applications, featuring components like large language models, frameworks, vector databases, data extraction tools, and text embeddings. Additionally includes insights into Shopify's tech stack architecture and job opportunities in AI and software engineering.
204
1
4
Article
SwirlAI·51w
Breaking into AI Engineering in 2025.
A comprehensive roadmap for becoming an AI Engineer in 2025, covering essential skills from Python fundamentals and LLM APIs to advanced topics like AI agents, RAG systems, and observability. The guide emphasizes learning fundamentals while building practical skills, starting with basic LLM integration and progressing through vector databases, prompt engineering, agentic systems, infrastructure deployment, and security considerations. Key recommendations include mastering FastAPI and Pydantic, understanding different LLM types and structured outputs, implementing RAG with proper data preprocessing, and learning agent design patterns like ReAct and task decomposition.
204
2
5
Article
ByteByteGo·47w
EP169: RAG vs Agentic RAG
RAG (Retrieval Augmented Generation) combines information retrieval with large language models, but traditional RAG has limitations in adaptability and real-time processing. Agentic RAG introduces AI agents that make decisions, select tools, and refine queries for more accurate responses. The comparison covers Kubernetes fundamentals including control planes, nodes, and key resources like Pods and Deployments. Six space-efficient data structures are highlighted: Bloom Filter, HyperLogLog, Cuckoo Filter, Minhash, SkipList, and Count-Min Sketch. Database normalization forms from 1NF to 4NF are explained for eliminating redundancy and enforcing data integrity.
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6
Article
Machine Learning Mastery·50w
Implementing Vector Search from Scratch: A Step-by-Step Tutorial
A comprehensive tutorial demonstrating how to build a vector search engine from scratch using Python. Covers the three core steps of vector search: converting text to numerical vectors, calculating similarity using cosine similarity, and retrieving the most relevant results. Includes practical code examples with NumPy and Matplotlib, visualization of vector spaces, and explains the connection to RAG systems. The implementation uses simplified word embeddings and averaging techniques to make concepts accessible while maintaining the fundamental principles of semantic search.
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7
Article
Hacker News·47w
The New Skill in AI is Not Prompting, It's Context Engineering
Context Engineering emerges as a more comprehensive approach than prompt engineering for building effective AI agents. Rather than focusing solely on crafting perfect prompts, it involves designing dynamic systems that provide LLMs with the right information, tools, and format at the right time. The concept encompasses system prompts, user inputs, conversation history, long-term memory, retrieved information (RAG), available tools, and structured outputs. The key difference between basic and sophisticated AI agents lies not in code complexity but in context quality - successful agents gather comprehensive contextual information before generating responses, while failures often stem from inadequate context rather than model limitations.
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8
Article
Daily Dose of Data Science | Avi Chawla | Substack·48w
10 MCP, RAG and AI Agents Projects
A curated collection of 10 advanced AI engineering projects covering MCP-powered applications, RAG systems, and AI agents. Projects include video RAG with exact timestamp retrieval, corrective RAG with self-assessment, multi-agent flight booking systems, voice-enabled RAG agents, and local alternatives to ChatGPT's research features. The repository contains 70+ hands-on tutorials focusing on real-world implementations of LLMs, memory-enabled agents, multimodal document processing, and performance optimization techniques like binary quantization for 40x faster RAG systems.
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9
Article
Hacker News·50w
Fine-Tuning LLMs is a Huge Waste of Time
Fine-tuning advanced LLMs for knowledge injection is counterproductive because it overwrites existing valuable information stored in densely interconnected neurons. Instead of adding knowledge, fine-tuning risks destroying the carefully built ecosystem of an already trained model. Better alternatives include retrieval-augmented generation (RAG), adapter modules like LoRA, and contextual prompting, which inject new information without damaging the underlying model's knowledge base. These modular approaches preserve the integrity of pre-trained networks while achieving the desired knowledge enhancement goals.
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Video
AI Engineer·48w
Building AI Agents that actually automate Knowledge Work - Jerry Liu, LlamaIndex
Jerry Liu from LlamaIndex presents a framework for building AI agents that automate knowledge work over unstructured documents. He distinguishes between assistive agents (chat interfaces that help humans get information) and automation agents (background processes that handle routine tasks). The approach requires a comprehensive document toolbox with parsing capabilities for complex PDFs, Excel sheets, and other formats, plus appropriate agent architectures ranging from constrained to unconstrained workflows. Real-world applications include financial due diligence, enterprise search, and technical data sheet processing, with LlamaIndex providing cloud services for document parsing that outperform existing benchmarks.
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Article
Daily Dose of Data Science | Avi Chawla | Substack·49w
Deploy any ML model, RAG or Agent as an MCP server
LitServe now supports MCP (Model Context Protocol) integration through a dedicated endpoint, allowing any ML model, RAG system, or AI agent to be deployed as an MCP server. This eliminates the need for custom integration code for each application. The implementation involves defining input schemas, setup methods, and inference logic in a simple Python class structure. The article also covers a 4-part MCP crash course and demonstrates deploying a Qwen 3 Agentic RAG system using CrewAI, Firecrawl, and LitServe.
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12
Article
Javarevisited·48w
Top 6 Udemy Courses to Learn Vector Databases for AI and LLM Projects (2025)
Vector databases have become essential for AI applications involving large language models, recommendation engines, and retrieval-augmented generation systems. Six Udemy courses are recommended for learning vector databases like Pinecone, FAISS, ChromaDB, and Qdrant in 2025. The courses range from beginner-friendly fundamentals to advanced AI engineering, covering practical implementations with Python, LangChain integration, RAG system development, and real-world AI project building. These affordable courses provide hands-on experience with modern vector database tools and their applications in semantic search, chatbots, and GenAI products.
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Article
Meilisearch·48w
How to Build a RAG Pipeline: A Step-by-Step Guide
RAG (Retrieval-Augmented Generation) pipelines combine search engines with large language models to provide accurate, grounded responses by retrieving relevant information before generating answers. The guide covers building a complete RAG system from data ingestion and chunking through embedding generation, vector storage with Meilisearch, and integration with generative models. Key considerations include choosing appropriate tools, optimizing chunking strategies, monitoring performance, managing costs, and implementing security measures for production deployments.
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Article
WebDev·51w
Simplifying Document Parsing in Node.js Projects
A developer created doc-extract, a unified Node.js library that simplifies document parsing by handling multiple formats (PDF, DOCX, DOC, PPT, PPTX, TXT) through a single API. The library supports file paths and buffers, returns metadata like page count and word count, and is fully typed with TypeScript. It's particularly useful for LLM pipelines, RAG implementations, and document search tools.
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Article
PHP Dev·48w
NeuronAI ADK: PHP Framework for supercharge Your PHP Apps with Autonomous AI Agents
NeuronAI ADK is an open-source PHP framework designed specifically for integrating AI agents into PHP applications. Built natively for PHP 8.1+, it supports popular frameworks like Laravel, Symfony, and WordPress, connects to various LLM providers including OpenAI and Claude, and includes features like RAG implementation, built-in tools, scaling capabilities with Redis and RabbitMQ, and comprehensive observability options.
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