Llm

Introduction Designing systems around large language models (LLMs) is not just about calling an API. Once you go beyond toy demos, you face questions like: How do I keep latency under control as usage grows? How do I manage costs when token usage explodes? How do I make results reliable and safe enough for production? How do I deal with context limits, memory, and personalization? How do I choose between hosted APIs and self-hosting? This post is a zero-to-hero guide to system design for LLM-powered applications. It assumes you’re comfortable with web backends / APIs, but not necessarily a deep learning expert. ...

Introduction Retrieval-Augmented Generation (RAG) has revolutionized how large language models (LLMs) handle knowledge-intensive tasks by combining retrieval from external data sources with generative capabilities. Unlike traditional LLMs limited to their training data, RAG pipelines enable models to access up-to-date, domain-specific information, reducing hallucinations and improving accuracy.[1][3][7] This blog post dives deep into RAG pipelines, exploring their architecture, components, implementation steps, best practices, and production challenges, complete with code examples and curated resource links. ...

Introduction The promise of large language models (LLMs) is compelling: intelligent agents that can handle customer interactions, provide guidance, and automate complex tasks. Yet in practice, developers face a critical challenge that no amount of prompt engineering can fully solve. An AI agent that performs flawlessly in testing often fails spectacularly in production—ignoring business rules, hallucinating information, and delivering inconsistent responses that damage brand reputation and customer trust.[3] This gap between prototype and production is where Parlant enters the picture. Built by Emcie, a startup founded by Yam Marcovitz and staffed by engineers and NLP researchers from Microsoft, Check Point, and the Weizmann Institute of Science, Parlant is an open-source framework that fundamentally rethinks how developers build conversational AI agents.[3] Rather than fighting with prompts, Parlant teaches agents how to behave through structured, programmable guidelines, journeys, and guardrails—making it possible to deploy agents at scale without sacrificing control or compliance.[3] ...

Large Language Models (LLMs) are moving from research labs into production systems at an incredible pace. As soon as organizations move beyond simple API calls to third‑party providers, a question appears: “How do we run LLMs ourselves, reliably, and at scale?” For many teams, the answer is: Kubernetes. This article dives into Kubernetes for LLMs—when it makes sense, how to design the architecture, common pitfalls, and concrete configuration examples. The focus is on inference (serving), with notes on fine‑tuning and training where relevant. ...

Introduction As large language models (LLMs) have grown into the tens and hundreds of billions of parameters, full fine‑tuning has become prohibitively expensive for most practitioners. Two techniques—LoRA and QLoRA—have emerged as leading approaches for parameter-efficient fine‑tuning (PEFT), enabling high‑quality adaptation on modest hardware. They are related but distinct: LoRA (Low-Rank Adaptation) introduces small trainable matrices on top of a frozen full‑precision model. QLoRA combines 4‑bit quantization of the base model with LoRA adapters, making it possible to fine‑tune huge models (e.g., 65B) on a single 24–48 GB GPU. This article walks through: ...