Category: Rag
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RAG with Hybrid Search: How Does Keyword Search Work?
RAG with Hybrid Search: How Does Keyword Search Work? Understanding keyword search, TF-IDF, and BM25 The post RAG with Hybrid Search: How Does Keyword Search Work? appeared first on Towards Data Science. Maria Mouschoutzi Go to original source
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Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop
Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop A practical guide to choosing between single-pass pipelines and adaptive retrieval loops based on your use case’s complexity, cost, and reliability requirements The post Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop appeared first on Towards Data Science. Mostafa…
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Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale
Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale Reducing LLM costs by 30% with validation-aware, multi-tier caching The post Zero-Waste Agentic RAG: Designing Caching Architectures to Minimize Latency and LLM Costs at Scale appeared first on Towards Data Science. Partha Sarkar Go to original source
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How Cursor Actually Indexes Your Codebase
How Cursor Actually Indexes Your Codebase Exploring the RAG pipeline in Cursor that powers code indexing and retrieval for coding agents The post How Cursor Actually Indexes Your Codebase appeared first on Towards Data Science. Kenneth Leung Go to original source
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You Probably Don’t Need a Vector Database for Your RAG — Yet
You Probably Don’t Need a Vector Database for Your RAG — Yet Numpy or SciKit-Learn might meet all your retrieval needs The post You Probably Don’t Need a Vector Database for Your RAG — Yet appeared first on Towards Data Science. Thomas Reid Go to original source
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When Does Adding Fancy RAG Features Work?
When Does Adding Fancy RAG Features Work? Looking at the performance of different pipelines The post When Does Adding Fancy RAG Features Work? appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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Retrieval for Time-Series: How Looking Back Improves Forecasts
Retrieval for Time-Series: How Looking Back Improves Forecasts Why Retrieval Helps in Time Series Forecasting We all know how it goes: Time-series data is tricky. Traditional forecasting models are unprepared for incidents like sudden market crashes, black swan events, or rare weather patterns. Even large fancy models like Chronos sometimes struggle because they haven’t dealt…
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Prompt Engineering vs RAG for Editing Resumes
Prompt Engineering vs RAG for Editing Resumes Running a code-free comparison in Azure The post Prompt Engineering vs RAG for Editing Resumes appeared first on Towards Data Science. Robert Etter Go to original source
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Chunk Size as an Experimental Variable in RAG Systems
Chunk Size as an Experimental Variable in RAG Systems Understanding retrieval in RAG systems by experimenting with different chunk sizes The post Chunk Size as an Experimental Variable in RAG Systems appeared first on Towards Data Science. Sarah Schürch Go to original source
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The Geometry of Laziness: What Angles Reveal About AI Hallucinations
The Geometry of Laziness: What Angles Reveal About AI Hallucinations A story about failing forward, spheres you can’t visualize, and why sometimes the math knows things before we do The post The Geometry of Laziness: What Angles Reveal About AI Hallucinations appeared first on Towards Data Science. Javier Marin Go to original source
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How to Do Evals on a Bloated RAG Pipeline
How to Do Evals on a Bloated RAG Pipeline Comparing metrics across datasets and models The post How to Do Evals on a Bloated RAG Pipeline appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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How to Perform Agentic Information Retrieval
How to Perform Agentic Information Retrieval Learn how to utilize AI agents to find information in your document corpus The post How to Perform Agentic Information Retrieval appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Build an Over-Engineered Retrieval System
How to Build an Over-Engineered Retrieval System Which is actually how some people do it The post How to Build an Over-Engineered Retrieval System appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k
How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k The third and final part for evaluating the retrieval quality of your RAG pipeline with graded measures The post How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k appeared first on Towards Data Science. Maria Mouschoutzi Go to…
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How to Build Agents with GPT-5
How to Build Agents with GPT-5 Learn how to use GPT-5 as a powerful AI Agent on your data. The post How to Build Agents with GPT-5 appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Multi-Agent SQL Assistant, Part 2: Building a RAG Manager
Multi-Agent SQL Assistant, Part 2: Building a RAG Manager A hands-on guide to comparing multiple RAG strategies — Keyword, FAISS, and Chroma The post Multi-Agent SQL Assistant, Part 2: Building a RAG Manager appeared first on Towards Data Science. Alle Sravani Go to original source
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How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP)
How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) Evaluating the retrieval quality of your RAG pipeline with binary, order-aware measures The post How to Evaluate Retrieval Quality in RAG Pipelines (part 2): Mean Reciprocal Rank (MRR) and Average Precision (AP) appeared first on Towards Data…
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Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources
Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources Why do few chatbots return figures from source documents in their responses? The post Building a Multimodal RAG That Responds with Text, Images, and Tables from Sources appeared first on Towards Data Science. Partha Sarkar Go to original source
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Graph RAG vs SQL RAG
Graph RAG vs SQL RAG Evaluating RAGs on graph and SQL databases The post Graph RAG vs SQL RAG appeared first on Towards Data Science. Reinhard Sellmair Go to original source
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Bringing Vision-Language Intelligence to RAG with ColPali
Bringing Vision-Language Intelligence to RAG with ColPali Unlocking the value of non-textual contents in your knowledge base The post Bringing Vision-Language Intelligence to RAG with ColPali appeared first on Towards Data Science. Julian Yip Go to original source
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Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI
Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI Context engineering, semantic layers, and the evolution of retrieval for agentic AI The post Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI appeared first on Towards Data Science. Steve Hedden Go to original source
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How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k
How to Evaluate Retrieval Quality in RAG Pipelines: Precision@k, Recall@k, and F1@k In my previous posts, I have walked you through putting together a very basic RAG pipeline in Python, as well as chunking large text documents. We’ve also looked into how documents are transformed into embeddings, allowing us to quickly search for similar documents…
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RAG Explained: Reranking for Better Answers
RAG Explained: Reranking for Better Answers How reranking improves retrieval-augmented generation by surfacing the most relevant results The post RAG Explained: Reranking for Better Answers appeared first on Towards Data Science. Maria Mouschoutzi Go to original source
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How to Select the 5 Most Relevant Documents for AI Search
How to Select the 5 Most Relevant Documents for AI Search Improve the document retrieval step of your RAG pipeline The post How to Select the 5 Most Relevant Documents for AI Search appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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RAG Explained: Understanding Embeddings, Similarity, and Retrieval
RAG Explained: Understanding Embeddings, Similarity, and Retrieval Let’s take a closer look at how the retrieval mechanism works The post RAG Explained: Understanding Embeddings, Similarity, and Retrieval appeared first on Towards Data Science. Maria Mouschoutzi Go to original source
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Evaluating Your RAG Solution
Evaluating Your RAG Solution A guide to building and evaluating RAG solutions by leveraging LLM-as-a-Judge capabilities. The post Evaluating Your RAG Solution appeared first on Towards Data Science. Alex Davis Go to original source
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Why Context Is the New Currency in AI: From RAG to Context Engineering
Why Context Is the New Currency in AI: From RAG to Context Engineering Context, not computation, is the real currency of intelligent systems The post Why Context Is the New Currency in AI: From RAG to Context Engineering appeared first on Towards Data Science. Sudheer Singamsetty Go to original source
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How to Context Engineer to Optimize Question Answering Pipelines
How to Context Engineer to Optimize Question Answering Pipelines Learn how to apply context engineering to enhance your question answering systems. The post How to Context Engineer to Optimize Question Answering Pipelines appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques
How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques Optimize your AI search with RAG, contextual retrieval and evaluations The post How to Scale Your AI Search to Handle 10M Queries with 5 Powerful Techniques appeared first on Towards Data Science. Eivind Kjosbakken Go to original source
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Agentic RAG Applications: Company Knowledge Slack Agents
Agentic RAG Applications: Company Knowledge Slack Agents Lessons learnt using LlamaIndex and Modal The post Agentic RAG Applications: Company Knowledge Slack Agents appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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New to LLMs? Start Here
New to LLMs? Start Here A guide to Agents, LLMs, RAG, Fine-tuning, LangChain with practical examples to start building The post New to LLMs? Start Here appeared first on Towards Data Science. ALESSANDRA COSTA Go to original source
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How to Build an AI Journal with LlamaIndex
How to Build an AI Journal with LlamaIndex This post will share how to build an AI journal with the LlamaIndex. We will cover one essential function of this AI journal: asking for advice. We will start with the most basic implementation and iterate from there. We can see significant improvements for this function when…
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Think. Know. Act. How AI’s Core Capabilities Will Shape the Future of Work
Think. Know. Act. How AI’s Core Capabilities Will Shape the Future of Work “It is not the strongest of the species that survives, nor the most intelligent, but the one most responsive to change.” – Charles Darwin, Originator of Evolutionary Theory Not long ago, I came across an article about a CEO, who was visibly…
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Agentic GraphRAG for Commercial Contracts
Agentic GraphRAG for Commercial Contracts In every business, legal contracts are foundational documents that define the relationships, obligations, and responsibilities between parties. Whether it’s a partnership agreement, an NDA, or a supplier contract, these documents often contain critical information that drives decision-making, risk management, and compliance. However, navigating and extracting insights from these contracts can…