A rag peddler, historically known as a rag-and-bone man, is an itinerant trader who collects discarded household items—specifically rags, old clothes, scrap metal, and bones—to sell for recycling or reuse. Active from the Middle Ages through the 20th century, these individuals, often living in poverty, traveled neighborhoods on foot or with a cart, paving the way for modern recycling.
The term “rag trade” originally referred to the clothing and textile industry, particularly in Britain. It encompasses everything from fabric production and dressmaking to the sale of clothing in local markets and high-end stores.
The Ragpicker represents one of the practitioners of a now-obsolete profession that involved sifting through the detritus of daily life—not only rags, which were sold to paper manufacturers—but also kitchen scraps, soap and other castoffs that were left out for trash collectors.
A rag-and-bone man or ragpicker (UK English) or ragman, old-clothesman, junkman, or junk dealer (US English), also called a bone-grubber, bone-picker, chiffonnier, rag-gatherer, rag-picker, bag board, or totter, collects unwanted household items and sells them to merchants.
A rag-and-bone man is a person who goes from street to street in a vehicle or with a horse and cart buying things such as old clothes and furniture. [British] Rag-and-bone men trot past on horse-drawn carts. regional note: in AM, use junkman, junk dealer.
Rag-and-bone men were collectors of discarded clothes, bones, and other low-value items that could be re-sold to merchants. The cloth was recycled to make shoddy and bones were used to make glue.
Migrants, illiterates, and unskilled persons who are lowest in the caste hierarchy and considered the poorest of all are mainly involved in the work of rag-picking (Majumder & Rajvanshi, 2017). Their lower caste, poverty, and occupation they follow attach stigma to their identity (Kumar, 2020).
Water and sewerage company performance for each metric is given a red, amber or green ( RAG ) status. Green status means that metric performance is on track to meet the relevant Water Industry Strategic Environmental Requirements ( WISER ) expectations.
Top Companies in Retrieval-augmented Generation (RAG) Market - AWS (US), Microsoft (US), Google (US), IBM (US) and Nvidia (US) MarketsandMarkets: The retrieval-augmented generation (RAG) market is projected to grow from USD 1.94 billion in 2025 to USD 9.86 billion by 2030 at a CAGR of 38.4% during the forecast period.
[Tottie, hotsy-totsy, tootsie, tootsy, toff] - OneLook. Usually means: Attractive person, often sexually appealing. ▸ noun: (UK, Ireland, slang) sexually attractive women considered collectively; usually connoting a connection with the upper class.
Though it is not as common as it once was, “sheila” is the Australian slang for girl or woman. It originally came from the Irish name Síle, which was exclusively used with women.
Conclusion. RAG was never the end goal, just the starting point. As we move into the agentic era, retrieval is evolving into a part of a full discipline: context engineering. Agents don't just need to find documents; they need to understand which data, tools, and memories are relevant for each step in their reasoning.
Cotton rags were in constant demand because of their ability to produce different grades of paper found on the market. 17 Paper mills depended on peddlers because of their efficient collection of rags.
RAG improves relevance and accuracy by using real-time enterprise data to generate informed responses, making it well-suited for dynamic environments with frequent data changes, while LLM fine-tuning excels in specialized tasks by embedding industry-specific language and knowledge into the model.
Rag pickers have respiratory problems such as sore throat, high prevalence of chronic obstructive pulmonary disease (COPD), coughing, asthma and loss of breathing. Neutrophils create inflammation that leads to respiratory problems when exposed to organic dust (Athanasiou et al, 2010, Woulter et al, 2002).
The RAG (Red, Amber, Green) rating system is widely used in healthcare to classify medications based on. prescribing responsibilities and settings. This classification helps ensure that medications are prescribed. appropriately according to the clinician's expertise and the patient's needs.
