How to Build an Agent Deployment Pipeline From Scratch
On a Friday afternoon, an engineer changes one sentence in a support agent’s system prompt to make replies more concise. Unit tests pass. The image...
Opencontroller by lyzr Blog10 min read
retrieval augmented generation
494 stories
How Do Word Embeddings Capture Relationships Between Words?
Understanding how words become related in a learned vector space The post How Do Word Embeddings Capture Relationships Between Words? appeared first on Towards Data Science.
Towards Data Science16 min read
How to Build Reliable RAG Systems?
A RAG demo takes an afternoon. A RAG system that gives correct answers on the thousandth query, from messy documents, for users who ask…Continue reading on Towards AI »
Towards AI1 min read
AI Data Infrastructure: Building a Governed Foundation for Enterprise AI
Trustworthy AI depends on data, not just models. Discover our data-first approach to AI infrastructure, governance, and retrieval for enterprises.
Towards AI12 min read
A saved reply is written for one customer. Your support bot can send it to anyone.
A common way to give a support bot knowledge is to let your agents save their good answers. A ticket gets solved, the agent clicks save, and the answer joins a set the bot retrieves from when a similar question comes in. Many support tools offer some version of it. We built ours.
Dev.to AI7 min read
How to do question answering from a PDF with Python and RAG
Build a Python pipeline that answers questions from PDFs and links each answer to its page, then check the citations before you trust them.
Apify Blog29 min read
Model collapse: why AI models degrade and how to prevent it
AI models trained on AI-generated data lose rare words and facts first. Learn what the research shows, see our GPT-2 test, and build a pipeline that keeps real data in every round.
Apify Blog17 min read
Gemini 4 Argon Hands-On Review: Capabilities and Performance
Google’s Gemini 4 Argon promises stronger capabilities across creative, analytical, and document-heavy tasks. But how well does it perform in practice? From generating videos and building websites to analyzing data and reconciling conflicting documents, the model offers plenty to explore.
Analytics Vidhya9 min readFrustratingly Easy Agentic RAG via Portable Skills: A Case Study in Customer Support
Enterprise workflow automation typically requires retrieving a complete and accurate set of relevant information from large operational document corpora (SOPs, policy documents) given a specific scenario or request.
arXiv - cs.AI2 min readTraining-Free BioNEL-RAG: Advancing Biomedical Entity Linking through Retrieval-Augmented Generation and Large Language Model
Biomedical Named Entity Linking (BioNEL) is the task of mapping mentions extracted from biomedical texts to canonical entities or concepts in the standardized knowledge base.
arXiv - cs.CL (NLP)2 min readLongHisDoc: A Comprehensive Benchmark for Chinese Long Historical Document Understanding
Chinese historical documents are invaluable cultural treasures, but their complexity poses significant comprehension challenges for non-specialists. Despite the success of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) in modern document tasks, their capabilities in Long…
arXiv - cs.CL (NLP)2 min readTAI #226: The AI Model Race Needs Better Tests
Also, Meta’s Muse Wonder, Claude Haiku 5.5, OpenAI’s Ultrafast tier, Mistral Large 4, Reflection Beam, and new rivals to Jev.
Towards AI17 min read
RAG vs Fine-Tuning: Which One Does Your Business Chatbot Actually Need?
"Can you train ChatGPT on our documents?" is one of the most common requests we get. It's a fair question, but it usually mixes up two different techniques. In most cases what the business needs isn't training at all. It's retrieval-augmented generation, or RAG.
Dev.to AI6 min read
Log Files Are Untrusted Input - A Red Team Found Out the Hard Way
A team red-teaming their own log-reading AI assistant found something that should worry anyone who's bolted an LLM onto an internal tool: planted instructions inside log lines could get the model to embed a database password in a Markdown link.
Dev.to AI7 min read
Natural language security queries at Orca Security using Amazon Bedrock
Orca Security built Discovery AI, a natural language query agent on Amazon Bedrock that converts plain-English questions into Sonar queries. The post shares the multi-stage architecture and the results: 90% lower query cost, 93% fewer input tokens, and 75% lower latency than the initial baseline.
AWS ML Blog11 min readAdvanced RAG Chunking: Hierarchical, Semantic, and Contextual Retrieval
Part 5 of the Production RAG Series — Small-to-Big Retrieval, Bounded Semantic Splitting, Breadcrumbs, and Anthropic’s Contextual Retrieval.
Towards AI12 min read
LangChain Explained in Telugu 🔥 | Build AI Agents from Scratch | LangGraph, RAG, MCP & LangSmith
ChatGPT answers questions… but how do we build AI systems that can actually complete tasks? What should I explain next — Comment below! 👇 In this video, we're exploring LangChain from scratch using one practical real-world example.
Withme Sravani
Where do LLMs actually earn their place in a recommender system? Kim Falk’s new book + 5 ebook giveaway [D]
Hi r/MachineLearning, Stjepan from Manning here. The mods approved this post, and I appreciate being welcomed back to share another book with you. We’ve just launched AI Recommender Systems: Transformers, LLMs, agents, and RAG by Kim Falk in MEAP, our early-access program.
Reddit - r/ML2 min read
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