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Search LLM Integration , found 56 results

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Advanced LLM and AI Agent Development

sickn33/antigravity-awesome-skills

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

Advanced LLM and AI Agent Development

sickn33/antigravity-awesome-skills

Expertise in building production-grade LLM applications, advanced Retrieval-Augmented Generation (RAG) systems, and complex intelligent agents. This skill covers the entire AI lifecycle, including model orchestration, vector search optimization, multimodal integration (vision/audio), MLOps deployment, and robust safety governance for enterprise-level AI solutions.

AI Product Engineering

sickn33/antigravity-awesome-skills

Helps teams ship reliable AI-powered products by detailing LLM integration patterns, RAG architecture, scalable prompt engineering, UX resilience, and cost-aware design.

On-Device LLM Integration for Apple Platforms

foundation-models-on-device

affaan-m/everything-claude-code

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

GPTQ Quantization Guide

Orchestra-Research/AI-Research-SKILLs

GPTQ enables post-training 4-bit quantization for large LLMs, delivering up to 4× memory reduction and 3-4× faster inference with under 2% perplexity loss on consumer GPUs through AutoGPTQ, PEFT, and Transformer integrations.

Groq CI/CD Integration Guide

groq-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

Configure robust CI/CD pipelines for Groq API integrations using GitHub Actions. This guide covers setting up unit tests (mocked), live integration tests, and automated model deprecation checks. It ensures that your LLM-powered application remains stable and compatible as models evolve.

Groq Local Development Workflow Setup

groq-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to setting up a fast, reproducible local development workflow for Groq applications. This setup includes structured project scaffolding, implementing singleton clients, defining model constants, and providing isolated unit tests with mocking, alongside live integration tests. Ideal for developers building low-latency LLM applications and ensuring rapid iteration cycles.

Groq LLM Migration Deep Dive

groq-migration-deep-dive

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide for migrating Large Language Model (LLM) integrations from providers like OpenAI or Anthropic to Groq. It details technical steps including SDK replacement, model ID mapping, building provider abstraction layers, and implementing zero-downtime traffic shifting using feature flags. This ensures developers can leverage Groq's superior speed while maintaining service continuity.

Multi-Environment Groq Setup Guide

groq-multi-env-setup

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to configuring and managing Groq API access across development, staging, and production environments. This skill establishes environment-specific configurations, ensuring optimal model selection, rate limit handling, and secure secret management (using AWS, GCP, Vault) for robust and scalable LLM integrations.

Implementing Groq SDK Patterns

groq-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to production-ready patterns for using the Groq SDK in TypeScript and Python. Covers best practices for client singleton setup, type-safe completions, streaming, robust error handling (rate limiting, API errors), and implementing exponential backoff retries to ensure stable, high-performance LLM integration.

LangChain Architecture for LLM Applications

langchain-architecture

sickn33/antigravity-awesome-skills

Master the LangChain framework, the industry standard for building sophisticated and production-grade Large Language Model (LLM) applications. This skill covers core concepts including autonomous agents, multi-step chains, memory management, Retrieval-Augmented Generation (RAG), and external tool integration, enabling the development of complex, stateful AI systems.

LangChain CI/CD Integration and Testing

langchain-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive CI/CD pipeline designed for LangChain applications. It automates quality assurance through fast, mocked unit tests and slower, more accurate integration tests utilizing real LLMs (e.g., OpenAI). It specifically validates the entire RAG pipeline and integrates tracing with LangSmith, ensuring robust deployment quality.

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