Latest AI and tech news
Part 1: Databricks RAG Architecture and Fundamentals...
Irrelevant chunks, hallucinated answers, no audit trail: fix all three by syncing SharePoint via Graph API, indexing with hybrid search in Azure AI Search, and orchestrating with Jinba.
Keyword search fails when query and document vocabulary diverge. This guide shows how to embed docs into Pinecone, filter by metadata, and generate grounded LLM answers via Jinba RAG workflows.
A developer queries an internal AI assistant. The assistant looks up the documentation, finds a relevant troubleshooting document, and provides an answer....
Part 5 of The RAG Field Guide: RAG explained plainly, for the curious and the technical....
Cohere Embed 5 Pro indexes documents while Fast serves queries. Their shared embedding space offers a practical retrieval trade-off....
Mem0 made four decisions for you. Here is how to own every one of them with one SQL table, one Python class, and an update policy you can…...
A LlamaIndex vector store is where your index keeps node embeddings, usually alongside the text chunks and metadata, so queries can find similar content. The default store lives in memory, so a restart or redeploy wipes the index and sends you back t...
Like many technical disciplines before it, GRC is shifting left. To keep pace with modern cloud development and mounting regulations, embedding continuous compliance earlier in the software development lifecycle is no longer optional – it’s mandatory...
I was working on a content extraction pipeline for a RAG implementation that ingests regulatory filings and legal documents to create a knowledge repository. The pipeline processes ingested files, runs them through an LLM, and produces structured sum...
A few months ago, our RAG (Retrieval-Augmented Generation) setup did what most first versions do: the Streamlit app called OpenAI directly, pulled chunks from MongoDB, and stitched an answer together. It worked. It also had three problems nobody want...
Most anti-fraud architectures in enterprise payment workflows evaluate transactions in isolation....
This guide explains how to fine-tune an LLM step by step, compares full fine-tuning with LoRA and QLoRA, and shows when prompting or RAG is the better way to improve a model's outputs.
How a context layer differs from a data catalog, a knowledge graph, and RAG, what each one does, and when you need more than one.
A tiny seasonal collection: pumpkin, pinecone, ghost, mushroom, chestnut, apple, acorn, and maple leaf — each with its own face. Small autumn decorations, gifts, or party favors. Separate monocolor profiles for individual figures (10.6h, 8 plates). S...
AI services are becoming a fast-growing attack surface in production environments. The processes that run your models, agents, and inference pipelines tend to rely on open-source dependencies, ranging from AI-specific frameworks like LangChain and Hu...
We loaded 990,000 OpenAI embeddings of 1,536 dimensions into six databases that index vectors themselves, built an HNSW index in each, and measured five things: time to a queryable index, storage and memory, recall against throughput, throughput unde...
Build a RAG pipeline for enterprise PDFs and Word docs using LlamaCloud for layout-aware parsing and Jinba to wire parsing, retrieval, Azure OpenAI generation, and audit logging into one workflow.
After 25+ years partnering with customers to unlock teamwork, we’ve seen firsthand the hardest challenge isn’t executing the task, but connecting the right context to the flow of work. And as AI takes on more work, the advantage isn’t a smarter model...
Why traditional retrieval metrics miss meaningful similarities...
In a 1,203-question test, a company brain on FalkorDB scored 80.0 vs 39.2 for retrieval with the same model. Here is how Graph Hacks teams built one.
Retrieval-augmented generation (RAG) has quickly become one of the most useful patterns in the burgeoning world of enterprise AI. Rather than fine-tuning a model on private data, a RAG system instead extracts text from internal documents, splits tha...
GraphRAG-SDK · FalkorDB · Entity resolution Entity resolution: how to make your knowledge graph accurate at scale Apple Inc., AAPL and Apple are one company,
From tokens and embeddings to RAG, AI agents’ tool use, and prompt injection, learn the essential concepts behind building AI apps that…...
A practical guide to RAG chunking strategies, retrieval metrics, and controlled experiments for building more reliable AI applications....
Discover how enterprise knowledge bases centralize company information, empower AI-driven ...
This guide explains what text embeddings are, how they turn text into vectors that capture meaning, and how to choose, evaluate, and apply embedding models to search, RAG, and other real-world use cases.