Learn LLM fundamentals, AI engineering, RAG, MLOps, fine-tuning, and deployment with five practical courses designed to help you become a stronger AI and machine learning engineer.
Automating 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...
AI agents can handle large context windows, yet still forget what happened after a session ends. Memory systems close that gap by preserving useful facts, preferences, relationships, and task state, giving agents continuity beyond a single prompt. Open-source projects now approach memory through AP...
Sarvam 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. Insuranc...
GraphRAG 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.
Good Architecture Deletes the Signals Your Agent Depends On
Every boundary you draw removes a signal your tooling was relying on. That is a structure problem, not a search problem.
The post Good Architecture Deletes the Signals Your Agent Depends On appeared first on Towards Data Science.
AI Slop Is in Your Training Dataset Now. I Tested Three Ways to Spot It.
My AI detectors flagged many genuine reviews, and filtering them made the sentiment model less accurate.
The post AI Slop Is in Your Training Dataset Now. I Tested Three Ways to Spot It. appeared first on Towards Data Science.
Agentic Context Engineering (ACE): Self-Improving Language Models
Agentic Context Learning or ACE is a learning paradigm that lets an AI agent improve across tasks by editing the context it reads, while leaving model weights unchanged. The paper outlining the techniques show why full rewrites fail, how the playbook update works, and where the measured gains hold u...
Inside a transformer, token index is a coordinate. Paragraph structure is what turns it into a metric.
The post Your LLM Has a Curved Space of Paragraphs appeared first on Towards Data Science.
10 Things I’m Learning Beyond AI to Become More Technologically Fluent
Part 1: Understanding the technologies shaping our future
The post 10 Things I’m Learning Beyond AI to Become More Technologically Fluent appeared first on Towards Data Science.
Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals.
The post Your Model's MSE Is Lying to You: Part II appeared first on Towards Data Science.
RAG Isn't an Agent — I Built the Layer Between Retrieval and Action
RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.
The post RAG Isn't an Agent — I Built the Layer Between Retrieval and Action appeared first on Towards Data Science.
10 Solved Generative AI Projects to Boost your Profile
Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability. This guide compiles 10 solved projec...
A visual guide to MCP that explains how it works, how to use it with Claude Code, Tavily, GitHub, and Playwright, and what is new through simple diagrams that make the whole concept easy for anyone to understand.
Beyond RAGs: Building Actually Truthful AI Harnesses
Retrieval is not evidence. How to build AI that proves its own claims.
The post Beyond RAGs: Building Actually Truthful AI Harnesses appeared first on Towards Data Science.
GPT-6 Sol and Luna: Near-Astra Performance at Half the Price?
What happens when a frontier model’s abilities get packed into cheaper, faster versions? That’s what OpenAI did with GPT-6 Sol and GPT-6 Luna, two new models built with methods similar to GPT-6 Astra. OpenAI is cutting API prices for both models by 50% compared with their GPT-5.6 versions, and says ...
When the Correct Answer Is Nothing, What Does Your Pipeline Return?
The reliability mechanisms we add to LLM pipelines are often the ones that make them confidently wrong.
The post When the Correct Answer Is Nothing, What Does Your Pipeline Return? appeared first on Towards Data Science.
I Trained a Tiny Network to Compress Data. It Drew a Pentagon.
Reproducing Anthropic's "Toy Models of Superposition" from scratch in NumPy, with hand-derived gradients and no borrowed numbers.
The post I Trained a Tiny Network to Compress Data. It Drew a Pentagon. appeared first on Towards Data Science.
This article pulls together every verifiable number and detail from Anthropic's announcement, the platform documentation, the system card, and independent coverage, so you have one place to check the facts.
How GRPO Trains Small Language Models with Verifiable Rewards
The mechanics behind local reasoning experiments with Unsloth and why the reward function matters as much as the model.
The post How GRPO Trains Small Language Models with Verifiable Rewards appeared first on Towards Data Science.