llm
10 articles tagged with "llm"
Tech Feeds
Evals Aren’t a One-Time Report: Build a Living Test Suite That Ships With Every Release.
Continuous evaluation in production (monitoring, regressions, evals in CI/CD) You finally shipped that generative AI feature, and the initial manual testing looked spectacular. A few weeks later, use...
How to QA Test Your AI Agent: A Practical Playbook for 2026
How to QA Test Your AI Agent: A Practical Playbook for 2026 You shipped your AI agent. It works great in demos. Then it hits production and starts hallucinating tool arguments, ignoring instructions...
p-e-w/heretic: Fully automatic censorship removal for language models
Fully automatic censorship removal for language models
I kept hitting the /'quota wall/' with AI coding tools. So I built a router.
If you use OpenClaw, Codex, Cursor, or Claude Code, you've probably seen the same thing: your premium model quota disappears halfway through the week. I finally looked at my own usage logs and the cau...
Secrets Management for LLM Tools: Don’t Let Your OpenAI Keys End Up on GitHub 🚨
/'A practical guide to securing LLM API keys, embeddings, vector TL;DR: If you're building with LLMs and you're not treating secrets as first-class infrastructure, you're already at risk. Every week...
From print to digital: Making weekly flyers shoppable at Instacart through computer vision and LLMs
Decoupling the AI Stack: How to Architect a Production-Grade Local LLM System
From /'Localhost/' to /'On-Premise/': An open-source blueprint for building a privacy-first, scalable AI infrastructure with vLLM and LiteLLM. We are currently living in the /'Golden Age/' of Local AI. Tool...
Understanding the Entity Synonym Mapper in RASA
Our previous blog: Understanding RASA pipelines Hereafter, we'll dive deeper into how entities are normalized in RASA and how the Entity Synonym Mapper works, with YAML examples and practical insight...
How to Write an Oscar-Worthy LLM Prompt: Your Guide to the Prompt-Chaining Framework
This article introduces the RTRI framework—Role, Task, Rules, and Input/Output—as a structured approach to prompt engineering for LLMs. It emphasizes the importance of clear instructions and demonstrates how to enhance AI responses through prompt chaining, ultimately improving output quality and relevance.