#rag Development Tutorials & Tips | Roast Dev

100daysofcode 100daysofdevops 100pay 10mistakes 10yearworkanniversary 11 11labs 11tly 11ty 127001

Growing the Tree: Multi-Agent LLMs Meet RAG, Vector Search, and Goal-Oriented Thinking - Part 2

Simulating Better Decision-Making in Insurance and Care Management Through RAGIn the Part 1 post, I walked through how I built a CLI that runs multi-agent conversations in a social media (Reddit-inspi...

04.05.2025 0 Read More

Building a RAG System with Firebase Functions, OpenAI, and Pinecone

Few months back, I built the GenQL tool to generate SQL queries from natural text with the ability to provide context of database schema of my database to the AI. Here, I'm sharing the concepts of a b...

04.05.2025 0 Read More

How AI Agents Are Getting Smarter: MCP, ReAct, RAG & A2A Explained Simply

Artificial Intelligence is everywhere — from chatbots in customer service to voice assistants on your phone. But the way AI works is rapidly evolving, and it’s no longer just about answering quest...

04.05.2025 0 Read More

Build a knowledge graph from documents using Docling

First experience of building a knowledge graph from a document using Docling! Introduction and motivation Knowledge graphs are powerful tools for representing information in a structured way. ...

23.04.2025 0 Read More

LLM Agent Architecture for Scalable Company Summaries

At RiddleStory, we're building an online onboarding platform that helps new hires get up to speed faster and feel more connected from day one. But to create meaningful onboarding experiences, we need ...

24.04.2025 0 Read More

Beyond Keywords: Introducing MindsDB Knowledge Bases for RAG and Semantic Search

Unlocking the vast potential hidden within unstructured text data is still quite a challenge today. While enterprises often excel at managing structured information in databases, insights trapped acro...

24.04.2025 0 Read More

Document Loading, Parsing, and Cleaning in AI Applications

Welcome to part one of our Agentic RAG Best Practices series, where we cover how to load, parse, and clean documents for your agentic applications. This comprehensive guide will teach you how to build...

24.04.2025 0 Read More

Implementing Simple RAG in local environment /w .NET (C#).

This tutorial demonstrates how to build a simple Retrieval-Augmented Generation (RAG) solution using: Ollama LLM (Gemma 3) mxbai large embedding model PostgreSQL with the pgvector extension Pokémo...

25.04.2025 0 Read More

What the Heck Are Hybrid Knowledge Bases? (And Why They Matter for LLM Apps)

If you're building with LLMs and trying to give your agents or copilots real context, you've probably used a vector database.They're great for unstructured data — PDFs, HTML, markdown, text blobs. B...

25.04.2025 0 Read More

Part 1: The Memento Problem with AI Memory

We're drowning in takes about AI "memory." RAG is hailed as the silver bullet, promising intelligent systems that learn and retain information. But let's be brutally honest: most implementations are b...

25.04.2025 0 Read More

Growing the Tree: Multi-Agent LLMs Meet RAG, Vector Search, and Goal-Oriented Thinking - Part 2

Building a RAG System with Firebase Functions, OpenAI, and Pinecone

How AI Agents Are Getting Smarter: MCP, ReAct, RAG & A2A Explained Simply

Build a knowledge graph from documents using Docling

LLM Agent Architecture for Scalable Company Summaries

Beyond Keywords: Introducing MindsDB Knowledge Bases for RAG and Semantic Search

Document Loading, Parsing, and Cleaning in AI Applications

Implementing Simple RAG in local environment /w .NET (C#).

What the Heck Are Hybrid Knowledge Bases? (And Why They Matter for LLM Apps)

Part 1: The Memento Problem with AI Memory

#reading

#popular