Why Do LLMs Lie?
In this article, we will look at why this problem of hallucinations happens with LLMs and the techniques that can help make LLMs more dependable for answering.
ByteByteGo — System Design and Tech12 min read
retrieval augmented generation
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How to Design Architectural Guardrails Around AI Agents
Agent design patterns every data engineer must know The post How to Design Architectural Guardrails Around AI Agents appeared first on Towards Data Science.
Towards Data Science14 min readHow We Built an AI Agent That Remembers with Hindsight
Why Autonomous SRE Needs Episodic Memory: How We Built Opsight with Hindsight Every on-call engineer knows the sinking feeling of getting paged at 3:14 AM for a SEV-1 outage that feels eerily familiar, yet nobody can remember which config flag or database lock script resolved it last time.
Dev.to AI10 min readBuild a documentation chatbot with LangChain, OpenAI, Pinecone, and Apify
Your users know the answer is somewhere in the docs, but they’re still stuck. Build an AI chatbot that helps them find the right instructions and figure out what to do next.
Apify Blog14 min readBest Tools for AI Agent Guardrails in 2026
AI agent guardrails are relatively straightforward to evaluate when an agent only generates text. The decision gets more complicated as soon as the agent can...
Opencontroller by lyzr Blog18 min readAutomating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM
In this article, you will learn how to automatically extract structured knowledge from raw text and populate a knowledge graph with SPOC quads using a...
Machine Learning Mastery7 min readClaude Sonnet 5.5 Review: Faster Agentic Coding & Visual QA
A practical guide to what changed, the benchmarks that matter, the cost controls developers should not miss, and one hands-on demo worth building. Anthropic has just released Claude Sonnet 5.5. It is the middle child of the Claude family, and the one most people will actually use.
Analytics Vidhya9 min readPresentation: From Consumers to Builders: Turning 200 of our Team into Agent Creators in 2 Weeks
Ben Maraney shares how Forter demystified AI agent creation for technical and non-technical staff. He discusses leveraging custom MCP servers, combining no-code and code-based platforms, sidestepping complex RAG setups, and aligning security and legal teams to accelerate internal agent adoption…
InfoQ - AI/ML/Data38 min readBlitzy’s autonomous coding bet: Every codebase is already a graph
Knowledge graphs are moving to the center of autonomous software development as enterprises push coding agents beyond quick fixes and into large, interconnected codebases. The more code an agent touches, the more it needs to know about everything that code connects to.
SiliconANGLE4 min readFrom Raw Data to Graph-Native AI
I’ve been thinking about a gap in the way we discuss graphs in AI. Most of the conversation starts after the graph already exists. We talk about graph neural networks, GraphRAG, graph agents, and graph foundation models.
O'Reilly Radar - AI/ML7 min readThink you need a better reranker? Check your retrieval first.
You built a search system using the latest, greatest models, and maybe you were thrilled with the outcome. But what The post Think you need a better reranker? Check your retrieval first. appeared first on The New Stack.
The New Stack - AI3 min readBest Tools for Deterministic vs. LLM-Judge Evaluation
An agent passes every offline eval in CI. You ship it. Production breaks within a week, because a class of prompts your eval dataset never...
Opencontroller by lyzr Blog16 min readAI Interview #7 - Why is this chatbot failing?
If your RAG pipeline is inaccurate, what should you do? This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 7 minutes. To crack more questions, subscribe to the channel!
GKCSSarvam Vision 2.1: The OCR Model Built for the Documents India Actually Has
For the first time, an OCR model reads Indian languages as fluently as English documents. Sarvam Vision 2.1 breaks a long-standing trade-off. Indian businesses had to choose between structural document parsing and script recognition. Never both. Finance teams automating invoices faced this.
Analytics Vidhya8 min readWhat Would A Serious AI Product Look Like?
Every “AI” tool is missing critical features that you would need if you wanted to do real work with them. What would those look like, if they existed?
Lobsters23 min readCompanies can build RAG in days. Making it reliable enough to run the business is much harder
A retrieval-augmented generation (RAG) demo is easy to get excited about.Read more
VentureBeat AI8 min readGraphRAG with TypeSafe Jev: A System One Approach to Scalable Knowledge Graphs
How calibrated decision models can handle high-frequency graph decisions while LLMs remain focused on reasoning, synthesis, and open-ended generation. The post GraphRAG with TypeSafe Jev: A System One Approach to Scalable Knowledge Graphs appeared first on Towards Data Science.
Towards Data Science16 min readHow to Give an LLM Long-Term Memory, and Why RAG Alone Is Not It
You give an LLM long-term memory by adding a layer outside the model that decides what is worth keeping from each conversation, stores it against the right person, updates it when a fact changes, and puts only the relevant slice back into the prompt on the next call.
Maximem Synap Blog19 min read
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