Implement LLM Reasoning with Semantic Kernel

This skill provides three Python sample implementations demonstrating how to build chain-of-thought and structured reasoning capabilities with large language models using Semantic Kernel. The examples show different approaches to enabling LLMs to break down complex problems into logical steps and produce reasoned outputs.

Use this skill when you need to implement multi-step reasoning workflows where an LLM must decompose complex queries, show its work, or follow structured thinking patterns. It's ideal when building applications that require transparent decision-making processes or when you need to integrate reasoning capabilities with Azure AI services or function calling mechanisms.

Do not use this if you only need simple, single-turn question-answering without intermediate reasoning steps. Avoid this approach when response latency is critical and the overhead of chain-of-thought processing would degrade user experience.

You provide Python code that implements one of three reasoning patterns: simple reasoning, Azure AI inference-based reasoning, or function calling-based reasoning. The samples accept prompts or queries that require multi-step logical processing.

You receive working Python implementations that demonstrate how to structure reasoning workflows, including the intermediate reasoning steps and final conclusions produced by the LLM.

The skill integrates with Azure AI Inference services, as demonstrated in the simple_reasoning_azure_ai_inference.py example. It also supports function calling patterns through the simple_reasoning_function_calling.py sample, enabling LLMs to invoke external functions as part of their reasoning process.

This skill is designed for Python developers building AI applications that require transparent, step-by-step reasoning capabilities. It serves ML engineers and application architects who need to implement chain-of-thought prompting patterns in production systems. Data scientists exploring structured reasoning approaches with LLMs will find concrete implementation examples. Developers working with Azure AI services who want to add reasoning layers to their existing inference pipelines will benefit from the Azure-specific sample.