#LLM
133 related articles
Logit Bias: Weighting LLM Vocabulary to Precisely Control Output
A deep dive into logit bias in LLMs: precisely control model word choice by adjusting token logit scores—boost specific words or suppress unwanted expressions, with OpenAI API code examples and usage boundary notes.
Adding Read-Only Guardrails to LLM-Generated SQL with sqlglot
Use sqlglot to parse SQL and build read-only guardrails for LLM-generated queries: SELECT-only validation, node blocking, file-read blocking, CTE-aware allowlists, row caps, cost proxy.
Inferring L1 Backgrounds from L2 English via LLM Token Probabilities: 70B Model Achieves SOTA
New study infers L1 backgrounds from L2 English using LLM token probabilities; Llama 3.1 8B and 70B evaluated on three datasets, with 70B achieving SOTA.
Sampling, Mixing, or Verifying? Optimal Allocation of Inference Compute Under Imperfect Verifiers
SPLIT uses a small amount of labeled pilot data to precisely plan inference-time compute allocation, approaching hindsight-optimal across seven benchmarks and 26 models, and reveals when more LLM reviewers can reduce accuracy.
Breakthrough in Training-Free Multilingual Text Embeddings: PRSR Enables More Precise Cross-Lingual Alignment in LLMs
This article introduces PRSR, a training-free multilingual text embedding method that removes language-specific variation via paired-residual subspace removal, improving cross-lingual alignment across 18 languages and 200+ evaluations.
Five Core Concepts to Learn Before Building LLM Applications
Learn the five concepts you must master before building LLM applications: machine learning basics, how LLMs work, generative vs. predictive AI, prompt engineering, and evaluation. Each concept includes a hands-on exercise to build solid intuition.
LLM Prompts Kept Failing in Production? 5 Rules to Make Them Rock-Solid
LLM prompts keep failing in production? This article breaks down temperature control, chain-of-thought ordering, five failure-mode defenses, constrained decoding for structured output, plus eval sets and retry chains—with a real fact-checking Agent iteration case—to help you build reliable AI applications.
Training LLMs with Set-Level Reinforcement Learning to Generate Diverse Scientific Ideas
A new paper proposes training LLMs with set-level reinforcement learning to generate diverse scientific ideas by pushing each new idea away from prior ones. An 8B model surpasses Claude Opus 4.8 and GPT-6 Astra in creative utility without sacrificing executability.
Large Language Models Discover New Quantum Circuits, Outperforming State-of-the-Art on Seven of Eight Tasks
Study proposes an LLM evolutionary framework that auto-designs quantum circuits, outperforming state-of-the-art on 7 of 8 tasks while reducing Toffoli gates and qubits, and discovering a novel reversible modular inversion architecture.
AI-Assisted Test Failure Analysis: Treating LLMs as a Diagnostic Layer, Not a Self-Healing Tool
GeekyAnts proposes using LLMs to analyze Playwright test failure artifacts while keeping execution deterministic and AI as a diagnostic classifier. This critical breakdown examines its architecture, redaction risks, hallucination issues, and self-healing test traps, with deployment recommendations.
OpenRouter Alternatives: Choosing Between an AI API Relay and a Self-Hosted Gateway
Compare OpenRouter, self-hosted LiteLLM, and Xinghuo AI Gateway — understand the ops responsibility gap between managed relays and self-hosted gateways, plus reproducible token cost reconciliation and API acceptance steps.
Turning Structured Output Into a Game: Hands-On with Token Arcade
Token Arcade turns structured output and constrained decoding into an interactive game. Through coin-flip and JSON-generation levels, it visually shows how LLMs mask disallowed tokens at each step, helping developers understand the mechanics behind structured output.
Understanding RAG: How to Make AI Answer Questions Using Your Own Data
A deep dive into how RAG (Retrieval-Augmented Generation) works: from document chunking and embedding to semantic retrieval and LLM answer generation, plus common pitfalls and real-world use cases.
Stop Trusting AI Providers Blindly: Two Steps to Close the Model Provenance and Privacy Gaps
Hosted AI APIs have two blind spots: model versions can be silently swapped and prompt destinations can't be verified. This article introduces a local proxy layer with model fingerprinting and prompt redaction to turn blind trust into conditional verification, and analyzes the limits of each approach.
Prompt Engineering in Practice: From Basics to Advanced LLM Prompt Templates
A systematic guide to prompt engineering from basics to advanced: prompt formula, zero-shot and few-shot, chain-of-thought, ReAct, tree of thoughts, meta-prompting, with copyable templates and an anti-pattern comparison table.
Cermin: An AI Engineering Practice for Building a Personal Thought Mirror with LLMs
Developer Arkoes07 built Cermin using a Telegram bot and LLMs to auto-capture and classify personal thoughts. Explore the architecture, kind/topic classification, and model selection decisions.
Top 5 AI Gateways Compared: How to Manage Production LLM Traffic
Deep comparison of Bifrost, Cloudflare, Vercel, LiteLLM, and OpenRouter AI gateways — covering performance, routing, reliability, cost control, and deployment for production LLM traffic management.
Stop Writing Prompts Like Prose: Use the Four-Part Contract to Kill LLM Drift
LLM outputs drifting between runs? The fix isn't better wording — it's structure. Learn the four-part prompt contract (Role, Task, Constraints, Output) with code validation to ship stable, debuggable prompts.
RAG or Fine-Tuning? You're Asking the Wrong Question When Building AI Systems
RAG or fine-tuning? Learn why this is a false dilemma. RAG solves knowledge problems; fine-tuning solves behavior problems. Production AI systems need both.
Open-Weight Model APIs in Practice: Selection Criteria and SDK Integration Guide
Learn the three ways to run open-weight AI models, key API selection criteria (protocol compatibility, precision, pricing), and code examples using OpenAI SDK, Anthropic SDK, and curl.