ai_llm_engineer_skill

Overview

This skill designs robust LLM architectures, writes production-grade prompts, and builds Retrieval-Augmented Generation (RAG) and LangChain applications. It focuses on maximizing context window use, reducing hallucinations, and hardening prompts against injection. The approach is pragmatic: measurable improvements to coherence, latency, and retrieval relevance.

How this skill works

It inspects use cases, dataset characteristics, and target LLM capabilities to recommend architecture patterns (RAG, streaming, hybrid). It generates structured, model-aware prompts using XML or Markdown hierarchies and enforces chain-of-thought only for complex reasoning. It also scaffolds LangChain flows, embedding pipelines, vector store choices, and safety checks for prompt injection and token overflow.

When to use it

• Building a RAG system for customer support or knowledge bases
• Designing prompts optimized per model family (Claude, GPT-4o, etc.)
• Implementing LangChain-based orchestration and retrieval pipelines
• Reducing hallucinations and improving answer traceability
• Hardening prompts and interfaces against adversarial or ambiguous inputs

Best practices

• Structure prompts with clear roles and context tags (XML or layered Markdown) to control instructions and system behavior
• Optimize for model-specific constraints: context window, temperature, and instruction-following strengths
• Use few-shot examples and explicit chain-of-thought only when necessary to improve complex reasoning
• Add defensive layers: input sanitization, instruction filters, and response validators to mitigate prompt injection
• Monitor token usage, tune chunking and retrieval scores to prevent token overflow and preserve relevance

Example use cases

• End-to-end RAG pipeline for internal documentation search with source attribution
• LangChain app that chains retrieval, LLM ranking, and deterministic post-processing
• Prompt library tailored to multiple models with conversion rules and temperature presets
• Automated prompt sanitization module for user-submitted queries
• Prototype that measures hallucination rate before/after prompt and retrieval changes