# information retrieval

Information retrieval is a computing field concerned with organizing, accessing, and ranking information so users can locate relevant items.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Your agent wants to search like a 2010 quant

DevFeed: [Your agent wants to search like a 2010 quant](<https://devfeed.tech/articles/your-agent-wants-to-search-like-a-2010-quant-12802.md>)

Original publisher: [Read original article](<https://blog.vespa.ai/your-agent-wants-to-search-like-a-2010-quant/>)

Author: Jon Bratseth

Published: 2026-07-07T00:00:00Z

Content type: opinion

Language: en

Sources: [Vespa Blog](<https://devfeed.tech/sources/vespa-blog.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [information retrieval](<https://devfeed.tech/topics/information-retrieval.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Google Search](<https://devfeed.tech/topics/google-search.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [genai](<https://devfeed.tech/tags/genai.md>), [google-search](<https://devfeed.tech/tags/google-search.md>), [hybrid-search](<https://devfeed.tech/tags/hybrid-search.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [rag](<https://devfeed.tech/tags/rag.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [search](<https://devfeed.tech/tags/search.md>)

### AI overview

The article argues that AI agents should retrieve information with more control and sophistication than ordinary human search users. It describes a progression from vector retrieval to hybrid search using methods such as BM25 and machine-learned ranking, and presents search as code as a possible next stage.

### Source excerpt

The idea of empowering AI agents to retrieve information like a professional is going mainstream.

## Scaling a Vespa Application: Feeding Fast and Furiously

DevFeed: [Scaling a Vespa Application: Feeding Fast and Furiously](<https://devfeed.tech/articles/scaling-a-vespa-application-feeding-fast-and-furiously-12797.md>)

Original publisher: [Read original article](<https://blog.vespa.ai/scaling-a-vespa-application-feeding-fast-and-furiously/>)

Author: Kai Borgen

Published: 2026-04-28T00:00:00Z

Content type: tutorial

Language: en

Sources: [Vespa Blog](<https://devfeed.tech/sources/vespa-blog.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [information retrieval](<https://devfeed.tech/topics/information-retrieval.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Homebrew](<https://devfeed.tech/topics/homebrew.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>), [onnx](<https://devfeed.tech/topics/onnx.md>), [optimum](<https://devfeed.tech/topics/optimum.md>), [XML](<https://devfeed.tech/topics/xml.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-search](<https://devfeed.tech/tags/ai-search.md>), [cli](<https://devfeed.tech/tags/cli.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [genai](<https://devfeed.tech/tags/genai.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [install](<https://devfeed.tech/tags/install.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [onnx](<https://devfeed.tech/tags/onnx.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rag](<https://devfeed.tech/tags/rag.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [tensors](<https://devfeed.tech/tags/tensors.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

This tutorial demonstrates how to scale a Vespa application while feeding the full MS_marco passages dataset. It covers preparing the dataset, configuring access, deploying a sample application, and using scaling and metrics to improve feed throughput and performance.

### Source excerpt

A tutorial on how to scale the resources in a Vespa application to increase feed throughput. Using the metrics dashboard for informed and optimised scaling.

## Silver lining playbook: Likely China-origin activity targeting US persons

DevFeed: [Silver lining playbook: Likely China-origin activity targeting US persons](<https://devfeed.tech/articles/silver-lining-playbook-likely-china-origin-activity-targeting-us-persons-41324.md>)

Original publisher: [Read original article](<https://openai.com/index/disrupting-malicious-uses-of-ai-silver-lining-playbook>)

Published: 2026-02-01T00:00:00Z

Content type: article

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [OpenAI](<https://devfeed.tech/topics/openai.md>), [ChatGPT](<https://devfeed.tech/topics/chatgpt.md>), [Social engineering](<https://devfeed.tech/topics/social-engineering.md>), [email](<https://devfeed.tech/topics/email.md>), [information retrieval](<https://devfeed.tech/topics/information-retrieval.md>)

Tags: [chatgpt](<https://devfeed.tech/tags/chatgpt.md>), [china](<https://devfeed.tech/tags/china.md>), [email](<https://devfeed.tech/tags/email.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [openai](<https://devfeed.tech/tags/openai.md>), [report](<https://devfeed.tech/tags/report.md>), [research](<https://devfeed.tech/tags/research.md>), [safety](<https://devfeed.tech/tags/safety.md>), [social-engineering](<https://devfeed.tech/tags/social-engineering.md>), [us](<https://devfeed.tech/tags/us.md>)

### AI overview

OpenAI describes banning a small set of likely China-origin ChatGPT accounts that used its models to research US persons and federal locations, draft social-engineering emails, and request guidance related to face-manipulation software. The activity was named "Silver Lining Playbook."

### Source excerpt

OpenAI banned likely China-origin accounts using AI to research US persons, locations, and social-engineering tactics.

## Baidu и AI Search Paradigm: мультиагентная структура для интеллектуального поиска информации

DevFeed: [Baidu и AI Search Paradigm: мультиагентная структура для интеллектуального поиска информации](<https://devfeed.tech/articles/baidu-ai-search-paradigm-24027.md>)

Original publisher: [Read original article](<https://habr.com/ru/companies/redmadrobot/articles/956570/>)

Author: redmadrobot (red\_mad\_robot)

Published: 2025-10-14T18:36:48Z

Content type: article

Language: ru

Sources: [Redmadrobot EN](<https://devfeed.tech/sources/redmadrobot-en.md>), [Redmadrobot RU](<https://devfeed.tech/sources/redmadrobot-ru.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [information retrieval](<https://devfeed.tech/topics/information-retrieval.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-search](<https://devfeed.tech/tags/ai-search.md>), [executor](<https://devfeed.tech/tags/executor.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [llm](<https://devfeed.tech/tags/llm.md>), [rag](<https://devfeed.tech/tags/rag.md>), [search](<https://devfeed.tech/tags/search.md>), [tag-d89cae10e887](<https://devfeed.tech/tags/tag-d89cae10e887.md>), [tag-e1c0c11ccefc](<https://devfeed.tech/tags/tag-e1c0c11ccefc.md>)

### AI overview

The article analyzes Baidu's AI Search Paradigm, a proposed multi-agent architecture for intelligent search built on large language models. It explains how specialized agents coordinate planning, tool use, execution, and answer writing, and contrasts this approach with classical information retrieval, semantic search, learning-to-rank, and RAG systems.

### Source excerpt

Аналитический центр red_mad_robot продолжает разбирать ключевые исследования в сфере интеллектуальных систем и генеративного поиска. На этот раз рассказываем про архитектуру AI Search Paradigm от Baidu -- новой системы интеллектуального поиска, построенной на LLM и мультиагентных методах. Читать далее

## Ep. 5: Key Techniques for Accurate AI-Driven Information Retrieval

DevFeed: [Ep. 5: Key Techniques for Accurate AI-Driven Information Retrieval](<https://devfeed.tech/articles/ep-5-key-techniques-for-accurate-ai-driven-information-retrieval-22253.md>)

Original publisher: [Read original article](<https://www.ardanlabs.com/blog/2024/07/key-techniques-for-accurate-ai-driven-information-retrieval-ep5.html>)

Published: 2024-08-22T00:00:00Z

Content type: tutorial

Language: en

Sources: [William Kennedy](<https://devfeed.tech/sources/william-kennedy.md>)

Topics: [information retrieval](<https://devfeed.tech/topics/information-retrieval.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [context](<https://devfeed.tech/topics/context.md>), [data](<https://devfeed.tech/topics/data.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [cohere](<https://devfeed.tech/topics/cohere.md>), [HTML](<https://devfeed.tech/topics/html.md>), [Website](<https://devfeed.tech/topics/website.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-and-web-page-content-extraction](<https://devfeed.tech/tags/ai-and-web-page-content-extraction.md>), [ai-driven-information-retrieval](<https://devfeed.tech/tags/ai-driven-information-retrieval.md>), [ai-models](<https://devfeed.tech/tags/ai-models.md>), [ai-models-and-text-data](<https://devfeed.tech/tags/ai-models-and-text-data.md>), [cohere](<https://devfeed.tech/tags/cohere.md>), [cohere-api-for-embeddings](<https://devfeed.tech/tags/cohere-api-for-embeddings.md>), [context](<https://devfeed.tech/tags/context.md>), [context-handling-in-ai](<https://devfeed.tech/tags/context-handling-in-ai.md>), [cosine-similarity-search](<https://devfeed.tech/tags/cosine-similarity-search.md>), [data](<https://devfeed.tech/tags/data.md>), [databases](<https://devfeed.tech/tags/databases.md>), [effective-ai-information-retrieval](<https://devfeed.tech/tags/effective-ai-information-retrieval.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [enhancing-ai-model-accuracy](<https://devfeed.tech/tags/enhancing-ai-model-accuracy.md>), [generative-ai-techniques](<https://devfeed.tech/tags/generative-ai-techniques.md>), [guide](<https://devfeed.tech/tags/guide.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [html](<https://devfeed.tech/tags/html.md>), [information-retrieval](<https://devfeed.tech/tags/information-retrieval.md>), [lancedb-vector-database](<https://devfeed.tech/tags/lancedb-vector-database.md>), [large-text-data-processing](<https://devfeed.tech/tags/large-text-data-processing.md>), [managing-large-scale-text-data-in-ai](<https://devfeed.tech/tags/managing-large-scale-text-data-in-ai.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [semantic-search-optimization](<https://devfeed.tech/tags/semantic-search-optimization.md>), [vector-embeddings-in-ai](<https://devfeed.tech/tags/vector-embeddings-in-ai.md>), [vectorization-techniques](<https://devfeed.tech/tags/vectorization-techniques.md>)

### AI overview

Episode 5 of an introductory Generative AI series explains techniques for retrieving relevant information from large text collections. It covers converting web content to Markdown, splitting text into overlapping chunks to preserve context, generating vector embeddings with Cohere's API, storing embeddings in vector databases such as LanceDB, and using cosine similarity for semantic search.

### Source excerpt

Introduction: Welcome to Episode 5 of our Intro to Generative AI series! In this episode, Daniel explores practical techniques for enhancing AI models' ability to handle large volumes of text data effectively. He addresses the challenges developers face when working with extensive content, such as entire web pages or internal documents, and provides actionable strategies to optimize the retrieval and processing of relevant information. Context Handling: Splitting large text into manageable chunks while preserving context. Vectorization Techniques: Converting text chunks into vector representations for semantic search. Semantic Search: Implementing cosine similarity to retrieve relevant information efficiently.

## Topic Modeling: Optimizing for Human Interpretability

DevFeed: [Topic Modeling: Optimizing for Human Interpretability](<https://devfeed.tech/articles/topic-modeling-15928.md>)

Original publisher: [Read original article](<https://developer.squareup.com/blog/topic-modeling-optimizing-for-human-interpretability>)

Author: Alyssa Wisdom

Published: 2017-12-20T22:38:24Z

Content type: tutorial

Language: en

Sources: [Square Corner Blog RSS Feed](<https://devfeed.tech/sources/square-corner-blog-rss-feed.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [data](<https://devfeed.tech/topics/data.md>), [information retrieval](<https://devfeed.tech/topics/information-retrieval.md>), [tokenization](<https://devfeed.tech/topics/tokenization.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [tokenization](<https://devfeed.tech/tags/tokenization.md>)

### AI overview

This article explains topic modeling as an unsupervised machine-learning method for identifying latent topics in large text collections. It discusses evaluating topic models, improving interpretability, preprocessing text, choosing the number of topics, and using document-term matrices.

### Source excerpt

Optimizing for Human Interpretability