Integrate LLM completions, RAG pipelines, and vector search into applications.
npx skills add https://github.com/johangm90/spex --skill spex-ai-eng
Please help me install this Agent Skill. Skill: spex-ai-eng Source: https://github.com/johangm90/spex/tree/main/skills/spex-ai-eng Command: npx skills add https://github.com/johangm90/spex --skill spex-ai-eng
This Skill streamlines the integration of AI features into your development workflow, ensuring robust LLM and RAG pipelines are built with a focus on quality, cost, and maintainability.
Use the spex-ai-eng skill to integrate a new AI feature by defining its contract and implementing the LLM integration.
High-intent search queries and answers about installing and using this skill.
You can integrate LLM completions and RAG pipelines by defining an API contract, implementing the LLM integration, and setting up vector database search. This ensures robust AI features are built with quality and maintainability.
The best way to monitor AI quality and cost is to develop and version prompts, create evaluation suites, and track metrics. This ensures your LLM features maintain high-quality responses without unexpected expenses.
Yes, this AI feature integration workflow supports both Python and TypeScript. It provides integrations for OpenAI, Anthropic, and Google AI to implement LLM-powered features across different development environments.
To set up vector search and evaluation suites for a chatbot, define its API contract, select an LLM, engineer prompts, and implement RAG pipelines. Configure evaluation metrics to ensure high-quality responses.
Yes, this approach supports integrations for OpenAI, Anthropic, and Google AI. It streamlines incorporating these platforms into your development workflow for LLM completions and RAG pipelines.
You need evaluation suites when building RAG pipelines to monitor AI quality and cost effectively. They help validate prompt versions and ensure your vector search integrations maintain robust, high-quality responses.
xurenluGuide developers in creating MCP servers with TypeScript and Python.
HemantSudarshanInstrument LLM calls for tracing and observability in Python or TypeScript pipelines.
qodex-aiBuild RAG applications with vector databases, embeddings, and retrieval strategies.
plurigridProvide structured prompts, RAG patterns, and LLM integration for AI applications.
