#rag Development Tutorials & Tips | Roast Dev

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Exploring RAG: Hypothetical Document Embeddings (HyDE)

Introduction Hypothetical Document Embeddings (HyDE) is a Retrieval-Augmented Generation (RAG) technique used in Large Language Models (LLMs). This blog explains why HyDE was developed and ho...

22.03.2025 0 Read More

Google Vertex RAG Engine with C# .Net

What is RAG? Ever asked an AI chatbot about your company's products or policies only to get a generic answer or worse - confidently incorrect information? This highlights the core limitation ...

22.03.2025 0 Read More

DIY — Building a Cost-Effective Questionnaire Automation with Bedrock

Security questionnaires are very common today. When customers consider your product, especially if you’re a startup, they often ask for proof that using your service is safe. This can become a huge ...

23.03.2025 0 Read More

Generic RAG Frameworks: Why They Can’t Catch On

In the market for generic RAG frameworks, the different providers are fighting over who can provide 67% accuracy versus 65%. And when you run an off-the-shelf RAG framework on your use case, it will e...

23.03.2025 0 Read More

How docs AI search works: Mintlify-Style with OpenAI Agents SDK

Documentation remains a critical resource for developers, serving as the definitive guide and knowledge base for any software product or service. However, traditional keyword-based searches often fall...

23.03.2025 0 Read More

Build AI Agent Memory From Scratch — Tutorial For Dummies

Ever wondered why some chatbots remember your name days later, while others forget what you said 5 minutes ago? This guide explains AI memory in super simple terms - no tech background needed! Have y...

24.03.2025 0 Read More

Integrating LlamaIndex and DeepSeek-R1 for reasoning_content and Function Call Features

In today's article, I'll show you how to use LlamaIndex's AgentWorkflow to read the reasoning process from DeepSeek-R1's output and how to enable function call features for DeepSeek-R1 within AgentWor...

24.03.2025 0 Read More

An overview of rules based ingestion in DataBridge

Rules! Rules! Rules! LLMs are exceptionally good at transforming content and extracting relevant information, making them ideal building blocks for robust document processing. However, without clear r...

24.03.2025 0 Read More

Building a RAG System With Claude, PostgreSQL & Python on AWS

TLDR: Build a complete RAG system using PostgreSQL, Claude, and AWS Bedrock - no specialized vector databases required. Use pgvector, pgai, and pgvectorscale extensions to handle embeddings and semant...

25.03.2025 0 Read More

A Frontend Dev's Guide for Learning AI

AI, and more specifically LLMs, have me really pumped. I am more excited today than I was when I first started working with SPAs and feeling the potential that client-side development opened up for us...

24.03.2025 0 Read More

Exploring RAG: Hypothetical Document Embeddings (HyDE)

Google Vertex RAG Engine with C# .Net

DIY — Building a Cost-Effective Questionnaire Automation with Bedrock

Generic RAG Frameworks: Why They Can’t Catch On

How docs AI search works: Mintlify-Style with OpenAI Agents SDK

Build AI Agent Memory From Scratch — Tutorial For Dummies

Integrating LlamaIndex and DeepSeek-R1 for reasoning_content and Function Call Features

An overview of rules based ingestion in DataBridge

Building a RAG System With Claude, PostgreSQL & Python on AWS

A Frontend Dev's Guide for Learning AI

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