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# rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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Couchbase Weekly Updates - May 2, 2025

Couchbase Weekly Updates - May 2, 2025

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1 min read
RAG - Retrieval-Augmented Generation, Making AI Smarter!

RAG - Retrieval-Augmented Generation, Making AI Smarter!

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5 min read
Power up your RAG chatbot with Snowflake Cortex Search Boosts and Decays

Power up your RAG chatbot with Snowflake Cortex Search Boosts and Decays

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7 min read
The Magic Behind LLM...!!

The Magic Behind LLM...!!

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Vector Recall Reasoning

Vector Recall Reasoning

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1 min read
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models

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

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2 min read
Vector Databases: their utility and functioning (RAG usage)

Vector Databases: their utility and functioning (RAG usage)

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12 min read
Building a Smart Café Menu Ordering Agent ☕🤖: Natural Language to Structured JSON with RAG

Building a Smart Café Menu Ordering Agent ☕🤖: Natural Language to Structured JSON with RAG

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

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

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3 min read
Improve Your Python Search Relevancy with Astra DB Hybrid Search

Improve Your Python Search Relevancy with Astra DB Hybrid Search

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11 min read
Build Code-RAGent, an agent for your codebase

Build Code-RAGent, an agent for your codebase

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

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

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3 min read
Building an E-Commerce Support Chatbot: Part 2 - Building the Knowledge Base

Building an E-Commerce Support Chatbot: Part 2 - Building the Knowledge Base

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2 min read
Configuring your own deep research tool (Using Nix Flakes)

Configuring your own deep research tool (Using Nix Flakes)

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4 min read
Building a Prompt-Based Crypto Trading Platform with RAG and Reddit Sentiment Analysis using Haystack

Building a Prompt-Based Crypto Trading Platform with RAG and Reddit Sentiment Analysis using Haystack

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4 min read
How to train LLM faster

How to train LLM faster

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3 min read
An overview of rules based ingestion in DataBridge

An overview of rules based ingestion in DataBridge

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6 min read
Integrating LlamaIndex and DeepSeek-R1 for reasoning_content and Function Call Features

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

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10 min read
AutoRAGLearnings: Hands-On RAG Pipeline Tuning with Greedy Search

AutoRAGLearnings: Hands-On RAG Pipeline Tuning with Greedy Search

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Part 1: The Memento Problem with AI Memory

Part 1: The Memento Problem with AI Memory

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

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

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

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

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5 min read
Document Loading, Parsing, and Cleaning in AI Applications

Document Loading, Parsing, and Cleaning in AI Applications

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16 min read
AI’s Hidden Superpower: Why Retrieval-Augmented Generation (RAG) is Game-Changing

AI’s Hidden Superpower: Why Retrieval-Augmented Generation (RAG) is Game-Changing

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3 min read
Implement an end-to-end RAG solution with watsonx.ai and Elasticsearch SQL

Implement an end-to-end RAG solution with watsonx.ai and Elasticsearch SQL

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2 min read
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