Skill UI

LitGPT provides clean, readable, and production-ready implementations for over 20 state-of-the-art LLMs (including Llama, Gemma, Phi, Mistral). It supports comprehensive workflows, including full fine-tuning, efficient LoRA/QLoRA adaptation, and pretraining from scratch. Ideal for developers needing academic understanding or robust, deployable model architectures.

Instructor lets you shape LLM responses into validated Pydantic models, automatically retrying failed extractions, parsing complex JSON with type safety, and streaming partial outputs across Anthropic, OpenAI, and other providers.

Knowledge Distillation is a crucial technique used to compress large, high-performing Language Models (Teacher) into smaller, more efficient Student models. This allows developers to deploy powerful AI capabilities (like those of GPT-4) on resource-constrained devices while significantly reducing inference costs. The method uses advanced techniques such as soft targets, temperature scaling, and Reverse KL Divergence (MiniLLM) to transfer knowledge effectively.

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.

This comprehensive guide serves as a quick reference for diagnosing and fixing the most frequent errors and exceptions encountered when developing with LangChain. It covers issues such as import failures, API authentication problems, structured output parsing errors (Zod), agent looping, and version mismatches, providing root causes and copy-paste solutions for reliable LLM application development.

Provides comprehensive guides for installing the LangChain SDK in both Python and Node.js environments. Covers the necessary steps to set up core packages and securely configure API keys for major LLM providers like OpenAI, Anthropic, and Google, enabling immediate development of complex AI applications.

This comprehensive guide walks developers through migrating complex LLM codebases from raw SDK calls (OpenAI, Anthropic) or legacy frameworks (LlamaIndex) to the structured LangChain framework. It covers code assessment, pattern-by-pattern migration, advanced agent construction, robust tool calling, and transforming custom RAG pipelines into scalable, standardized chains.

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

A comprehensive checklist for ensuring LangChain applications are robust, secure, and scalable for production environments. It covers critical areas including configuration management, advanced error handling, observability setup (e.g., LangSmith), performance optimization, security best practices (like prompt injection prevention and PII handling), rigorous testing, and zero-downtime deployment strategies. Use this guide when preparing for a major launch or auditing existing LLM systems.

This comprehensive skill implements advanced event-driven patterns for LangChain applications. It provides custom callback handlers to observe every stage of chain execution (e.g., tool start/end, LLM token generation). Furthermore, it supports real-time data transmission via Server-Sent Events (SSE) and WebSockets, and integrates webhooks for centralized event logging and comprehensive tracing.

Track and improve production LLMs with Langfuse observability: trace API calls, manage prompts, score evaluations, integrate with LangChain/LlamaIndex/OpenAI, and keep dashboards on cost, latency, and quality.

Integrate Langfuse into CI/CD pipelines (e.g., GitHub Actions) to automate the testing and validation of Large Language Models (LLMs). This guide covers prompt regression testing, tracing validation, and setting up experiment-driven quality gates, ensuring reliable and high-performing AI applications throughout the development lifecycle.