corpus
6 stories filed under corpus on Beyond Market Intelligence. The newest of them: “Migrate Between Embedding Models Without Rebuilding Your Entire Corpus”, “Design Your Data: Why a Simple FAQ Can Outperform RAG”, and “Explore language from the past with a focused AI built on vintage text.”. Backfilling a million vectors just to switch embedding models is the kind of cost that quietly stalls progress. Most RAG setups fight messy documents. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every corpus story on Beyond Market Intelligence, newest first.
Migrate Between Embedding Models Without Rebuilding Your Entire Corpus
Backfilling a million vectors just to switch embedding models is the kind of cost that quietly stalls progress. One developer found a smarter path: instead of re-embedding everything, pull a small set of documents from the old index and rerank them with the new model. With enough samples, retrieval quality matches native performance. In one test, just 50 documents closed the gap. That is practical, accessible innovation. The tooling supports Qdrant, pgvector, and FAISS, and it is ready to try.
Design Your Data: Why a Simple FAQ Can Outperform RAG
Most RAG setups fight messy documents. This piece flips that struggle into a design choice. A well-structured FAQ inverts the pipeline: parsing becomes trivial, retrieval acts as a cache, and few-shot prompting turns into a retrieval problem. It's a clever reframing that rewards intentionality. If you're tired of wrestling with chaotic corpora, this perspective offers a practical path forward. For a deeper look at how LLMs handle structure, our piece on paragraph navigation in token space pairs well with this.
Explore language from the past with a focused AI built on vintage text.
Three months and $807 bought Unbounded Labs a vintage LLM named Bart, trained from scratch on 20.1B tokens of pre-1931 English. That is a deliberate constraint, not a limitation. The team behind it wants to know if an AI can rediscover the conclusions of past scientists, as Demis Hassabis suggested, or if it is just predicting the next token. We find that question worth taking seriously, and their open-sourced benchmarks and datasets give us all a way to explore it.
Row-Level Chunks Deliver the Exact Table Data You Need
Tables are rarely retrieved in pieces. But when a document stores data in rows, each row carries its own context: the column headers that give it meaning. That row becomes a chunk, and it is often the exact unit the reader needs. Row-level chunks should be treated as a retrieval standard, not a workaround. It is a practical reframing of RAG, and it pairs well with our earlier look at how paragraph structure shapes token navigation.
Build AI Applications That Remember, Not Just Retrieve
RAG systems fetch, but they don't hold on to what matters. That's the gap this guide tackles head-on, offering a vendor-neutral blueprint for building a knowledge layer that actually accumulates understanding, not just query results. The Azure-native walkthrough, mapping Foundry, AI Search, Cosmos DB, and FastAPI to a property-insurance corpus, turns theory into something you can build on. If you're still questioning how much AI truly grasps, "Verify Your AI's Understanding" pairs well with this practical read. It's a grounded, actionable take.
Match Your Document to Its Best PDF Parser with Smart Dispatcher Logic
Before you hand your documents to an agentic RAG pipeline, you need to know how decisions get made. This closes the first brick by focusing on the dispatcher: it reads each PDF's nature, then picks the parsing method that fits. Fitz, Docling, PaddleOCR, EasyOCR, MinerU, or Surya are the options. The synthesis folds everything into one clean corpus. It's a practical, human-centered way to make complex parsing feel manageable.