#rag Development Tutorials & Tips | Roast Dev

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Build Your Own AI Chatbot: A Complete Guide to Local Deployment with ServBay, Python, and ChromaDB

In an era where data privacy is paramount, setting up your own local language model (LLM) provides a crucial solution for companies and individuals alike. This tutorial is designed to guide you throug...

14.03.2025 0 Read More

Understanding CAG (Cache Augmented Generation): AI's Conversation Memory With APIpie.ai

Ever noticed how your favorite AI assistant sometimes forgets what you were just talking about? Or how you need to keep reminding it of important context from earlier in your conversation? There's a s...

14.03.2025 0 Read More

Data Indexing and Common Challenges

At its core, data indexing is the process of transforming raw data into a format that's optimized for retrieval. Unlike an arbitrary application that may generate new source-of-truth data, indexing pi...

15.03.2025 0 Read More

Populating a RAG with data from enterprise documents repositories for Generative AI

Ingesting data from OneDrive into Milvus in order to use with generative ai. Image courtesy Milvus.io Introduction and motivation In a recent project we help our customer to populate a RAG vec...

15.03.2025 0 Read More

Overview: "Understanding LLMs: From Training to Inference"

Disclaimer: this is a report generated with my tool: https://github.com/DTeam-Top/tsw-cli. See it as an experiment not a formal research, 😄。 Mindmap Summary This paper provides a...

15.03.2025 0 Read More

AgentQL Enters the Agentic World with Langchain and LlamaIndex

AgentQL now integrates with Langchain and LlamaIndex, connecting AI agents and retrieval-augmented generation (RAG) models to the web. AgentQL extracts real-time, structured data from web pages and al...

16.03.2025 0 Read More

Overview: "OWASP Top 10 for LLM Applications 2025: A Comprehensive Guide"

Disclaimer: this is a report generated with my tool: https://github.com/DTeam-Top/tsw-cli. See it as an experiment not a formal research, 😄。 Mindmap Summary This document outline...

16.03.2025 0 Read More

Solutions Architect Agent using Knowledge Bases for Amazon Bedrock

What is Amazon Bedrock?Amazon Bedrock is a fully managed service provided by Amazon Web Services (AWS) designed to help developers build, scale, and deploy generative AI applications. It simplifies th...

16.03.2025 0 Read More

Improving RAG Systems with Amazon Bedrock Knowledge Base: Practical Techniques from Real Implementation

Recently, I integrated Amazon Bedrock Knowledge Base as a RAG (Retrieval-Augmented Generation) system into a chatbot, adding functionality that suggests multiple response options to the user's latest ...

17.03.2025 0 Read More

What if scaling context windows isn’t the answer to higher accuracy?

We’ve seen LLMs push context window limits to 1 million tokens. Impressive? Sure. But let’s get real: enterprise-scale AI systems demand more than brute force. Feeding terabytes of data into a mas...

17.03.2025 0 Read More

Build Your Own AI Chatbot: A Complete Guide to Local Deployment with ServBay, Python, and ChromaDB

Understanding CAG (Cache Augmented Generation): AI's Conversation Memory With APIpie.ai

Data Indexing and Common Challenges

Populating a RAG with data from enterprise documents repositories for Generative AI

Overview: "Understanding LLMs: From Training to Inference"

AgentQL Enters the Agentic World with Langchain and LlamaIndex

Overview: "OWASP Top 10 for LLM Applications 2025: A Comprehensive Guide"

Solutions Architect Agent using Knowledge Bases for Amazon Bedrock

Improving RAG Systems with Amazon Bedrock Knowledge Base: Practical Techniques from Real Implementation

What if scaling context windows isn’t the answer to higher accuracy?

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