Llm

Ollama is an open-source framework that enables running large language models (LLMs) locally on personal hardware, prioritizing privacy, low latency, and ease of use.[1][2] At its core, Ollama leverages llama.cpp as its inference engine within a client-server architecture, packaging models like Llama for seamless local execution without cloud dependencies.[2][3] This comprehensive guide dissects Ollama’s internal mechanics, from model management to inference pipelines, quantization techniques, and hardware optimization. Whether you’re a developer integrating Ollama into apps or a curious engineer, you’ll gain actionable insights into its layered design. ...

Introduction As language models are woven into more complex systems—multi-tool agents, retrieval-augmented generation, multi-model stacks—“what should handle this request?” becomes a first-class problem. That’s what a semantic router solves. Instead of routing based on keywords or simple rules, a semantic router uses meaning (embeddings, similarity, sometimes LLMs themselves) to decide: Which tool, model, or chain to call Which knowledge base to query Which specialized agent or microservice should own the request This post is a detailed, practical guide to semantic routers: ...

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] ...