#rag Development Tutorials & Tips | Roast Dev

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VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval

If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?Trick question, b/c most can’t. They treat retrieval as nearest-neighbor searc...

29.04.2025 0 Read More

A Developer’s Guide to Retrieval Augmented Generation (RAG) — How It Actually Works

If you're building AI applications and concerned about outdated knowledge in your models, Retrieval Augmented Generation (RAG) offers a smarter solution. Instead of relying solely on static training d...

29.04.2025 0 Read More

Build Code-RAGent, an agent for your codebase

Introduction Recently, I've been hooked up with automating data ingestion into vector databases, and I came up with ingest-anything, which I talked about in my last post. After Chonkie releas...

29.04.2025 0 Read More

Improve Your Python Search Relevancy with Astra DB Hybrid Search

Astra DB now supports hybrid search, which can increase the accuracy of your search by up to 45%. It does this by performing both vector search and BM25 keyword search and then reranking the results f...

29.04.2025 0 Read More

Retrieval-Augmented Generation (RAG): Giving AI a Supercharged Memory Boost

What is RAG? Retrieval-Augmented Generation (RAG) is a technique that lets AI models pull in real-world data before answering a question—like a student who can suddenly check a textbook dur...

30.04.2025 0 Read More

Vector Databases: their utility and functioning (RAG usage)

A reflection on vector databases and their usage by LLMs, by implementing a rudimentary one! Introduction TLDR;Vector databases have emerged as a critical infrastructure component in the era o...

30.04.2025 0 Read More

DeepRAG: Thinking to Retrieval Step by Step for Large Language Models

選定理由中国科学院ソフトウェア研究所とWeChat AI研究チームの共同研究。Paper: https://arxiv.org/abs/2502.01142 Code:N/A blog: https://x.gd/wTnkm https://zenn.dev/...

30.04.2025 0 Read More

How AI Understands Your Documents: The Secret Sauce of RAG

From Text to Intelligence: The AI's Learning Process Think of teaching a new employee how to do their job. You wouldn't: Dump all company manuals on their desk at once (oversaturation) Expect...

01.05.2025 0 Read More

RAG - Retrieval-Augmented Generation, Making AI Smarter!

Imagine asking a super-smart assistant a question, and instead of guessing, it quickly checks a library of trusted information to give you a spot-on answer. That’s what Retrieval-Augmented Generatio...

01.05.2025 0 Read More

The Magic Behind LLM...!!

Large Language Models (LLMs) are a type of artificial intelligence (AI) that can read, understand, and generate text like a human. They have become popular because they can do many things, like answer...

01.05.2025 0 Read More

VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval

A Developer’s Guide to Retrieval Augmented Generation (RAG) — How It Actually Works

Build Code-RAGent, an agent for your codebase

Improve Your Python Search Relevancy with Astra DB Hybrid Search

Retrieval-Augmented Generation (RAG): Giving AI a Supercharged Memory Boost

Vector Databases: their utility and functioning (RAG usage)

DeepRAG: Thinking to Retrieval Step by Step for Large Language Models

How AI Understands Your Documents: The Secret Sauce of RAG

RAG - Retrieval-Augmented Generation, Making AI Smarter!

The Magic Behind LLM...!!

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