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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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RAG in Space: How will astronauts survive on Mars without Googling?

RAG in Space: How will astronauts survive on Mars without Googling?

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5 min read
Granting autonomy to agents

Granting autonomy to agents

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13 min read
The 10 Top-Rated Talks about Knowledge Graphs

The 10 Top-Rated Talks about Knowledge Graphs

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2 min read
Analyzing Hugging Face Posts with Graphs and Agents

Analyzing Hugging Face Posts with Graphs and Agents

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12 min read
Implementing Retrieval-Augmented Generation with LangChain, Pgvector and OpenAI

Implementing Retrieval-Augmented Generation with LangChain, Pgvector and OpenAI

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1 min read
Optimizing MongoDB Hybrid Search with Reciprocal Rank Fusion

Optimizing MongoDB Hybrid Search with Reciprocal Rank Fusion

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3 min read
Overcoming LLM Testing Challenges with Pytest and Trulens: Ensuring Reliable Responses

Overcoming LLM Testing Challenges with Pytest and Trulens: Ensuring Reliable Responses

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8 min read
LlamaIndex RAG: Build Efficient GraphRAG Systems

LlamaIndex RAG: Build Efficient GraphRAG Systems

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8 min read
💡 What's new in txtai 8.0

💡 What's new in txtai 8.0

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11 min read
What C-Level Leaders Need to Know About AI Agents

What C-Level Leaders Need to Know About AI Agents

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9 min read
What is Agentic RAG? Building Agents with Qdrant

What is Agentic RAG? Building Agents with Qdrant

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15 min read
Large Language Models (LLMs)

Large Language Models (LLMs)

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1 min read
FalkorDB vs Neo4j

FalkorDB vs Neo4j

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2 min read
Exploring RAG: Benefits and Challenges Explained

Exploring RAG: Benefits and Challenges Explained

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2 min read
PDF chat with source highlights

PDF chat with source highlights

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1 min read
Top 10 Real-World Applications of Artificial Intelligence to Watch in 2025

Top 10 Real-World Applications of Artificial Intelligence to Watch in 2025

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5 min read
Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs

Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs

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1 min read
Retrieval Augmented Geese - Semantic Search with the HONC Stack

Retrieval Augmented Geese - Semantic Search with the HONC Stack

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9 min read
Contributing to ORAssistant

Contributing to ORAssistant

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3 min read
The Demise Of Human Coding, Rise Of AI, And Why It's Good For Devs Too

The Demise Of Human Coding, Rise Of AI, And Why It's Good For Devs Too

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4 min read
Comprehensive Guide to the Capabilities and Applications of Large Language Models (LLMs)

Comprehensive Guide to the Capabilities and Applications of Large Language Models (LLMs)

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12 min read
RedLM: My submission for the NVIDIA and LlamaIndex Developer Contest 03:00

RedLM: My submission for the NVIDIA and LlamaIndex Developer Contest

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38 min read
An easy way to remove PII before sending to LLMs

An easy way to remove PII before sending to LLMs

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2 min read
Specialized Domain Models: Unlocking the Power of Tailored AI Solutions

Specialized Domain Models: Unlocking the Power of Tailored AI Solutions

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6 min read
How to Stay Updated with the Latest Machine Learning Trends?

How to Stay Updated with the Latest Machine Learning Trends?

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4 min read
Think Smarter, Not Harder: Meet RAG

Think Smarter, Not Harder: Meet RAG

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6 min read
How to build a RAG model from scratch?

How to build a RAG model from scratch?

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6 min read
Langchain — RAG — Retrieval Augmented Generation