Social Media Ideas for Ml Research

LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships

Most LLM evaluation systems rely on vague scoring and human judgment disguised as metrics. I built a lightweight evaluation layer in pure Python that turns LLM outputs into reprodu...

ML IMP QUESTIONS DISCUSSION

Supervised LearningContinue reading on Medium »

5 Fun Papers That Explain LLMs Clearly

Want to understand LLMs better? Start with these five foundational papers that explain how they work.

5 Fun Papers That Explain LLMs Clearly

Want to understand LLMs better? Start with these five foundational papers that explain how they work.

An LLM as arbiter in RAG retrieval: picking the right candidate with reasons

Enterprise Document Intelligence [Vol.1 #7C] - One LLM call ranks the candidates with reasons. The output is one typed object your auditor can defend The post An LLM as arbiter in...

Can LLMs Replace Survey Respondents?

How unlearning fixes mode collapse in synthetic survey replies The post Can LLMs Replace Survey Respondents? appeared first on Towards Data Science.

How to Fine-Tune LFM2 Using QLoRA and DPO: A Complete Step-by-Step Coding Tutorial on Google Colab

Learn to fine-tune LFM2 with QLoRA, supervised fine-tuning, DPO, and adapter merging using TRL and PEFT on Colab. The post How to Fine-Tune LFM2 Using QLoRA and DPO: A Complete Ste...

LLM layer for a Rails application

Originally appeared on dmitrytsepelev.dev.Like it or not, a lot of applications are adding AI–native features: anything related to automated answers, object classification, knowled...

Machine Learning For Beginners: Everything You Need To Know

Machine Learning (ML) has become one of the most influential technologies driving innovation in today's digital world. From personalized recommendations on streaming platforms to f...

How to Build a Powerful LLM Knowledge Base

Use coding agents to power your knowledge base The post How to Build a Powerful LLM Knowledge Base appeared first on Towards Data Science.

LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does

In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...

From Regex to Vision Models: Which RAG Technique Fits Which Problem

Enterprise Document Intelligence [Vol.1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post From Regex to Vision Mo...

How AI Builds Reveal Research Handoff Gaps

Implementing Hybrid Semantic-Lexical Search in RAG

Implementing hybrid search strategies is a critical step in building modern RAG (Retrieval-Augmented Generation) systems , especially when shifting from prototype to production-rea...

Reliable LLM Inference at Scale

At Databricks, we’ve built a unique inference platform that serves every frontier...

Building Reliable Agentic AI Systems

One of the most interesting projects my colleagues have done with LLMs has been building a system with Bayer to allow pharmaceutical researchers to query decades of inf...

Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation

Why memorizing for the exam doesn't mean you understand the subject The post Water Cooler Small Talk, Ep. 11: Overfitting in RAG evaluation appeared first on Towards Data Science.

LLM Demo to Production: The Layers That Make an LLM Application Reliable

Taking an LLM from demo to production takes more than a better model.Continue reading on Medium »

From Local LLM to Tool-Using Agent

Using Gemma 4, Ollama, OpenAI Agents SDK, and Tavily MCP to build a lightweight research agent The post From Local LLM to Tool-Using Agent appeared first on Towards Data Science.

Context Engineering for RAG : The Four Typed Inputs Behind Every RAG Answer

Enterprise Document Intelligence [Vol.1 #7bis] - Tobi Lütke and Andrej Karpathy named the practice in 2025. For a single document, each brick emits typed pieces that converge on on...

Fine-tuning Language Models on Apple Silicon with MLX

Fine-tune open language models locally on your Mac using MLX. No cloud GPUs or costs required.

Fine-tuning Language Models on Apple Silicon with MLX

Fine-tune open language models locally on your Mac using MLX. No cloud GPUs or costs required.

Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each

Getting reliable, readable responses out of your LLM, and knowing which tool to reach for The post Structured Outputs with LLMs: JSON Mode, Function Calling, and When to Use Each a...

Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End

Enterprise Document Intelligence [Vol.1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel...