# ai hallucination (news sources) — RSS Amplifier Recent posts from the 1 feeds in the RSS Amplifier directory that cover ai hallucination. Page: Feed: --- ## [How to Format Your TDS Draft: A New and Improved Guide](https://towardsdatascience.com/how-to-format-your-tds-draft-a-new-and-improved-guide/) _2026-08-26 · TDS Editors · Towards Data Science_ Everything you need to know about creating a draft on our Contributor Portal The post How to Format Your TDS Draft: A New and Improved Guide appeared first on Towards Data Science . ## [A New Towards Data Science: A Faster Site and a Brand-New Contributor Portal](https://towardsdatascience.com/a-new-towards-data-science-a-faster-site-and-a-brand-new-contributor-portal/) _2026-08-25 · TDS Editors · Towards Data Science_ We're excited to share some big news: We completely rebuilt the TDS website and our contributor portal. Whether you come here to read, to write, or both, here's what you can expect from our new site. The post A New Towards Data Science: A Faster Site and a Brand-New Contributor Portal appeared first on Towards Data Science . ## [I Deployed My Data Pipeline to AWS. Then Everything That Was “Local” Broke.](https://towardsdatascience.com/i-deployed-my-data-pipeline-to-aws-then-everything-that-was-local-broke/) _2026-08-25 · Ibrahim Salami · Towards Data Science_ What moving a Dockerized pipeline off my laptop taught me about containers, networking, and hidden assumptions. The post I Deployed My Data Pipeline to AWS. Then Everything That Was “Local” Broke. appeared first on Towards Data Science . ## [Recursive CTEs: SQL’s Hidden Graph Traversal Engine](https://towardsdatascience.com/six-degrees-of-sql/) _2026-08-25 · Thomas Reid · Towards Data Science_ A practical guide to navigate hierarchies, find routes, detect cycles and calculate degrees of separation The post Recursive CTEs: SQL’s Hidden Graph Traversal Engine appeared first on Towards Data Science . ## [Hallucinations, Watermarks, Removers, and a Squeezed Balloon](https://towardsdatascience.com/hallucinations-watermarks-removers-and-a-squeezed-balloon/) _2026-08-25 · Javier Marín Valenzuela · Towards Data Science_ Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science . ## [One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries](https://towardsdatascience.com/one-document-type-a-million-files-structured-extraction-into-the-sql-table-rag-queries/) _2026-08-25 · Angela and Kezhan Shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #14C\] - One hour with two people, six to ten fields, and the two signals that separate a real column from one that will break a filter later The post One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries appeared first on Towards Data Science . ## [Put Your Own Logic Inside the Codex Agentic Loop](https://towardsdatascience.com/put-your-own-logic-inside-the-codex-agentic-loop/) _2026-08-24 · Shuai Guo · Towards Data Science_ Understanding Codex hooks The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science . ## [Can an LLM Forget the Right Things?](https://towardsdatascience.com/can-an-llm-forget-the-right-things/) _2026-08-24 · Anubhab Banerjee · Towards Data Science_ A hand-written CUDA inference runtime for Vision-Language-Action robots that decides what to remember, what to forget, and when it's simply too late to think. The post Can an LLM Forget the Right Things? appeared first on Towards Data Science . ## [Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash](https://towardsdatascience.com/speculative-decoding-on-cpus-nearly-4x-faster-token-generation-with-dflash/) _2026-08-24 · Ehssan Khan · Towards Data Science_ How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science . ## [10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong](https://towardsdatascience.com/10-positions-for-enterprise-rag-that-mainstream-tutorials-get-wrong/) _2026-08-24 · Kezhan Shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #M3\] - The ten positions the series argues from, and the map of every article that argues them The post 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong appeared first on Towards Data Science . ## [One platform, from silicon to agent (Sponsored)](https://crawlproof.com/a/MlTUENAiLyT4) _2026-08-24 · **Sponsored**_ Serverless inference plus Inference Router to optimize cost and latency at scale. ## [Building a Proper Backend for My LangGraph AI Agent](https://towardsdatascience.com/building-a-proper-backend-for-my-langgraph-ai-agent/) _2026-08-22 · Soner Yıldırım · Towards Data Science_ Turning a demo agent into something that can keep real booking data The post Building a Proper Backend for My LangGraph AI Agent appeared first on Towards Data Science . ## [Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline](https://towardsdatascience.com/multi-document-rag-a-folder-of-unrelated-pdfs-is-one-long-document-with-a-nested-outline/) _2026-08-22 · angela shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #14B\] - No shared fields means no index to build. One summary line per file plus each file’s own table of contents, and retrieval routes down two levels The post Multi-Document RAG: A Folder of Unrelated PDFs Is One Long Document with a Nested Outline appeared first on Towards Data Science . ## [Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call)](https://towardsdatascience.com/why-we-fine-tuned-siglip-and-why-thats-not-always-the-right-call/) _2026-08-22 · Miikka Silfverberg · Towards Data Science_ LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions. The post Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) appeared first on Towards Data Science . ## [Running Codex as a Headless Agent](https://towardsdatascience.com/running-codex-as-a-headless-agent/) _2026-08-21 · Shuai Guo · Towards Data Science_ Turning Codex from an interactive assistant into a programmable automation component The post Running Codex as a Headless Agent appeared first on Towards Data Science . ## [Estimating from No Data: Deriving a Continuous Score from Categories](https://towardsdatascience.com/estimating-from-no-data-deriving-a-continuous-score-from-categories-2/) _2026-08-21 · Elod Pal Csirmaz · Towards Data Science_ A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training The post Estimating from No Data: Deriving a Continuous Score from Categories appeared first on Towards Data Science . ## [Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG](https://towardsdatascience.com/retrieve-one-row-from-a-table-not-the-whole-table-row-level-chunks-for-rag/) _2026-08-21 · Kezhan Shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #7sexies\] - The unit of retrieval doesn’t have to be a page or a paragraph. When the corpus carries tables, each body row with its column headers is a chunk in its own right, and it’s often the one row the reader asked about The post Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG appeared first on Towards Data Science . ## [The Types of Dimensions in a Star Schema, and How to Use Them](https://towardsdatascience.com/what-are-the-types-of-dimensions-in-a-star-schema-and-how-to-use-them/) _2026-08-21 · Salvatore Cagliari · Towards Data Science_ Dimensions are one of the two main object types in dimensional modelling. But what are the different types of dimensions? And how can you use them? The post The Types of Dimensions in a Star Schema, and How to Use Them appeared first on Towards Data Science . ## [Bayesian Guardrails for AI Decisions: Measuring Uncertainty Before Automating Decisions](https://towardsdatascience.com/bayesian-guardrails-for-ai-decisions-measuring-uncertainty-before-automating-decisions/) _2026-08-21 · Mahe Jabeen Abdul · Towards Data Science_ AI systems should not automate a decision simply because they can provide a prediction. A decision system should consider how uncertain the prediction is and defer if a mistake would be costly. The post Bayesian Guardrails for AI Decisions: Measuring Uncertainty Before Automating Decisions appeared first on Towards Data Science . ## [How Benders Decomposition Works, Part II: Feasibility Cuts](https://towardsdatascience.com/how-benders-decomposition-works-part-ii-feasibility-cuts/) _2026-08-21 · Luis Fernando Pérez Armas · Towards Data Science_ Learning about Farkas' lemma and how it can inform Benders decomposition to learn from infeasibility, applied to the capacitated facility location problem. The post How Benders Decomposition Works, Part II: Feasibility Cuts appeared first on Towards Data Science . ## [How to Effectively Align Your Intent with Claude Code](https://towardsdatascience.com/how-to-effectively-align-your-intents-with-claude-code/) _2026-08-20 · Eivind Kjosbakken · Towards Data Science_ Improve your proficiency with Claude Code. The post How to Effectively Align Your Intent with Claude Code appeared first on Towards Data Science . ## [Persona-led AI publishing (Sponsored)](https://crawlproof.com/a/DdNYGLzLpyYL) _2026-08-20 · **Sponsored**_ Generate drafts, schedule subdomain publishing, and keep human review where needed. ## [The LLM Judge That Kept Agreeing With Itself](https://towardsdatascience.com/the-llm-judge-that-kept-agreeing-with-itself/) _2026-08-20 · Priyansh Bhardwaj · Towards Data Science_ What a production incident taught me about trusting a model to judge another model's work The post The LLM Judge That Kept Agreeing With Itself appeared first on Towards Data Science . ## [Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One](https://towardsdatascience.com/three-kinds-of-rag-corpus-and-what-it-costs-to-build-for-the-wrong-one/) _2026-08-20 · angela shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #14A\] - Three questions tell you which shape a document collection has, and each shape wants a different architecture The post Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One appeared first on Towards Data Science . ## [How to Fine-Tune an LLM: An End-to-End Guide](https://towardsdatascience.com/how-to-fine-tune-an-llm-an-end-to-end-guide/) _2026-08-20 · Sam Black · Towards Data Science_ A hands-on guide to fine-tuning LLMs for the real world The post How to Fine-Tune an LLM: An End-to-End Guide appeared first on Towards Data Science . ## [Making the Knowledge Layer a Graph You Actually Traverse](https://towardsdatascience.com/making-the-knowledge-layer-a-graph-you-actually-traverse/) _2026-08-20 · Miodrag Cekikj · Towards Data Science_ Why retrieval quality should be a property of the system, not of the question's wording? Rebuilding knowledge layer with graph traversal on every query, bitemporal edges, and two-threshold entity resolution. The post Making the Knowledge Layer a Graph You Actually Traverse appeared first on Towards Data Science . ## [How to Scale an Integration Pipeline Without Breaking Correctness](https://towardsdatascience.com/scaling-an-integration-pipeline-without-breaking-correctness/) _2026-08-19 · Yuelin Ou · Towards Data Science_ A production account of scaling an enterprise integration pipeline from 500 to 8,000 events per second, and the two correctness guarantees the throughput work was never allowed to trade away. The post How to Scale an Integration Pipeline Without Breaking Correctness appeared first on Towards Data Science . ## [Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality](https://towardsdatascience.com/kimi-k3s-1m-token-context-window-vs-rag-cost-latency-and-answer-quality/) _2026-08-19 · Sarah Schürch · Towards Data Science_ A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding. The post Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality appeared first on Towards Data Science . ## [Understanding Anti-AI Public Opinion](https://towardsdatascience.com/understanding-anti-ai-public-opinion/) _2026-08-19 · Stephanie Kirmer · Towards Data Science_ People can accept tradeoffs when they see value — but if they don’t, what happens? The post Understanding Anti-AI Public Opinion appeared first on Towards Data Science . ## [Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision](https://towardsdatascience.com/jigsaw-jeeves-building-a-puzzle-assistant-using-computer-vision/) _2026-08-19 · Chinmay Kakatkar · Towards Data Science_ Conceptual overview and walkthrough of a solution approach in Python The post Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision appeared first on Towards Data Science . ## [From Prototype to Production: The Architecture Behind Secure & Governed AI Agents](https://towardsdatascience.com/from-prototype-to-production-the-architecture-behind-secure-governed-ai-agents/) _2026-08-18 · Partha Sarkar · Towards Data Science_ Building the Responsible AI, security, and governance layers required for enterprise-ready agents The post From Prototype to Production: The Architecture Behind Secure & Governed AI Agents appeared first on Towards Data Science . ## [Building Enterprise Agent Systems that People can Trust, Verify and Improve](https://towardsdatascience.com/building-enterprise-agent-systems-that-people-can-trust-verify-and-improve/) _2026-08-18 · Sheila Teo · Towards Data Science_ 5 principles that determine whether an agent system succeeds in production, explained through one I built for a $100M+ company. The post Building Enterprise Agent Systems that People can Trust, Verify and Improve appeared first on Towards Data Science . ## [Stream Torrents & IPTV Instantly (Sponsored)](https://crawlproof.com/a/3bG0uW4fskB4) _2026-08-18 · **Sponsored**_ Search magnet links and stream movies, music, books, and live TV in-browser ## [Graph Engineering Isn’t About More Connections — It’s About Which Ones Get Used](https://towardsdatascience.com/graph-engineering-isnt-about-more-connections-its-about-which-ones-get-used/) _2026-08-18 · Emmimal P Alexander · Towards Data Science_ Adding more communication pathways between agents doesn’t necessarily improve multi-agent performance. In a controlled, reproducible experiment across 50 runs, recovery remained remarkably stable from 20% to 100% relationship density. But as the network became denser, the fraction of edges actually used fell sharply—revealing a gap between configured connectivity and behavioral connectivity. The… ## [Ten Is Not a Hundred](https://towardsdatascience.com/ten-is-not-a-hundred/) _2026-08-18 · Javier Marin · Towards Data Science_ The number that fooled every hallucination detector The post Ten Is Not a Hundred appeared first on Towards Data Science . ## [Webwright: Why AI Web Agents Should Write Code, Not Click](https://towardsdatascience.com/webwright-why-ai-web-agents-should-write-code-not-click/) _2026-08-17 · Chien Vu Minh · Towards Data Science_ For years, web agents have worked one click at a time—and often fallen apart on long tasks. Microsoft Research’s Webwright makes a different bet: give the model a terminal and let it write the program instead. On long-horizon tasks, the same GPT-5.4 model jumps from 33.5% to 60.1% success. And instead of leaving behind a click trace, it leaves something you can actually use again: a command-line… ## [Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them](https://towardsdatascience.com/three-generations-of-autoscaling-and-why-agentic-traffic-breaks-all-of-them/) _2026-08-17 · Shoumik Chakravarty · Towards Data Science_ How autonomous agents broke two decades of capacity planning — and what to build instead The post Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them appeared first on Towards Data Science . ## [How to Perform Effective Project Management with AI](https://towardsdatascience.com/how-to-perform-effective-project-management-with-ai/) _2026-08-17 · Eivind Kjosbakken · Towards Data Science_ Become a more productive software engineer with LLMs The post How to Perform Effective Project Management with AI appeared first on Towards Data Science . ## [Loop Engineering for RAG: The Small Loops Inside Each Step, the Big Loops Across the Pipeline](https://towardsdatascience.com/loop-engineering-for-rag-the-small-loops-inside-each-step-the-big-loops-across-the-pipeline/) _2026-08-17 · angela shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #13bis\] - The four bricks return useful results most of the time. Loop engineering is what the system does the rest of the time: when retrieval misses, when generation fails the schema, when the listing comes back incomplete, when an API call times out. Three control surfaces (trigger, termination, recovery) and one rule that separates a useful loop from a… ## [Designing a Persistent Knowledge Layer That Refuses to Guess](https://towardsdatascience.com/designing-a-persistent-knowledge-layer-that-refuses-to-guess/) _2026-08-16 · Miodrag Cekikj · Towards Data Science_ RAG Retrieves, It Never Remembers. A vendor-neutral blueprint for applications that accumulate understanding. Includes a complete Azure-native implementation (Microsoft Foundry, Azure AI Search, Cosmos DB, FastAPI) mapped to a property-insurance corpus. The post Designing a Persistent Knowledge Layer That Refuses to Guess appeared first on Towards Data Science . ## [Running SQL Concurrently Across Three Remote DuckDB Servers with Quack](https://towardsdatascience.com/running-sql-concurrently-across-three-remote-duckdb-servers-with-quack/) _2026-08-16 · Thomas Reid · Towards Data Science_ A small experiment in remote SQL execution The post Running SQL Concurrently Across Three Remote DuckDB Servers with Quack appeared first on Towards Data Science . ## [Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming](https://towardsdatascience.com/mathematical-experiments-are-becoming-abundant-through-human-machine-teaming/) _2026-08-15 · Sean Moran · Towards Data Science_ Two open problems, exact-arithmetic checking and a proof assistant, over a single weekend. The post Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming appeared first on Towards Data Science . ## [How to Shine as a Data Scientist in the Vibe Coding Era](https://towardsdatascience.com/how-to-shine-in-the-vibe-coding-era-as-a-data-scientist/) _2026-08-15 · Piero Paialunga · Towards Data Science_ Here's how to be the Data Scientist who thrives in a world where coding is a commodity. The post How to Shine as a Data Scientist in the Vibe Coding Era appeared first on Towards Data Science . ## [A Day in the Life of a Data Scientist in 2026](https://towardsdatascience.com/a-day-in-the-life-of-a-data-scientist-in-2026/) _2026-08-14 · Haden Pelletier · Towards Data Science_ How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science . ## [RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop](https://towardsdatascience.com/rag-workflow-and-loop-engineering-the-dispatcher-that-decides-when-to-loop-and-when-to-stop/) _2026-08-14 · angela shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #13\] - Putting the patterns together, and why this is what “agentic RAG” should look like The post RAG Workflow and Loop Engineering: The Dispatcher That Decides When to Loop and When to Stop appeared first on Towards Data Science . ## [My Model Was Cheating on Its Own Test](https://towardsdatascience.com/my-model-was-cheating-on-its-own-test/) _2026-08-14 · Abdullahi Dattijo · Towards Data Science_ A preprocessing pipeline let my car price model peek at the test set before the exam, and the twelve points of R squared it cheated its way to The post My Model Was Cheating on Its Own Test appeared first on Towards Data Science . ## [I Made an LLM Lay Siege to My Minecraft House](https://towardsdatascience.com/i-made-an-llm-lay-siege-to-my-minecraft-house/) _2026-08-14 · Jaemin Han · Towards Data Science_ Can a language model do live adversarial level design? Yes, emphasis on the adversarial part The post I Made an LLM Lay Siege to My Minecraft House appeared first on Towards Data Science . ## [How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs](https://towardsdatascience.com/how-to-utilize-okf-efficiently-to-enable-knowledge-exchange-among-llms/) _2026-08-13 · Anubhab Banerjee · Towards Data Science_ Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job — an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.5-Coder models (7B, 3B, 1.5B) — and shows the 28–37% TTFT reduction plus the one full-vocabulary equivalence check that keeps the whole thing safe.… ## [Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model](https://towardsdatascience.com/cut-an-enterprise-rag-pipelines-latency-and-cost-by-calling-the-llm-less-not-by-buying-a-faster-model/) _2026-08-13 · angela shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #9ter\] - The pipeline from Article 9 calls a model at several steps to be sure it is right. On easy questions that is needless latency. A per-question signal routes them past the model, about two seconds saved for a keyword match. The post Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model appeared first… ## [How to Orchestrate a Fleet of OpenClaw Bots](https://towardsdatascience.com/how-to-orchestrate-a-fleet-of-openclaw-bots/) _2026-08-13 · Eivind Kjosbakken · Towards Data Science_ Learn how to run OpenClaw bots for increased productivity The post How to Orchestrate a Fleet of OpenClaw Bots appeared first on Towards Data Science . ## [LangChain vs LangGraph: 4 Key Differences and When to Use Each](https://towardsdatascience.com/langchain-vs-langgraph-4-key-differences-and-when-to-use-each/) _2026-08-13 · Soner Yıldırım · Towards Data Science_ A practical guide to choose the proper tool for your agentic workflows and systems The post LangChain vs LangGraph: 4 Key Differences and When to Use Each appeared first on Towards Data Science . ## [Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From](https://towardsdatascience.com/before-full-agentic-rag-know-how-you-decide-and-the-parsing-methods-you-pick-from/) _2026-08-12 · angela shi · Towards Data Science_ Enterprise Document Intelligence \[Vol.1 #5nonies\] - Nature, plan, execute, synthesize: closing brick 1 with a dispatcher that reads each PDF’s nature and picks the method that fits, fitz, Docling, PaddleOCR, EasyOCR, MinerU or Surya, then folds the outputs into one corpus The post Before Full Agentic RAG: Know How You Decide, and the Parsing Methods You Pick From appeared first on Towards Data… ## [Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works](https://towardsdatascience.com/backpropagation-explained-for-beginners-part-3-how-backpropagation-really-works/) _2026-08-12 · Nikhil Dasari · Towards Data Science_ From one gradient to every gradient The post Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works appeared first on Towards Data Science .