Production AI Reasoning Systems

A production-ready implementation of AI reasoning systems with chain-of-thought prompting, intelligent strategy selection, cost monitoring, and enterprise deployment patterns.

Overview

This tutorial demonstrates how to build AI reasoning systems that intelligently route requests to appropriate reasoning strategies, saving 40-60% on costs while maintaining quality. Learn when chain-of-thought adds value and when it doesn't, based on research from Wharton, McKinsey, and Google.

Related Article: Production AI Reasoning Systems: When Chain-of-Thought Actually Matters

Key Features

• Intelligent Task Classification: Automatically determines if reasoning is needed
• Multiple Reasoning Strategies: Direct, CoT, Tree-of-Thoughts, Self-Consistency, Extended Thinking
• Cost Monitoring: Real-time tracking with budget alerts and optimization recommendations
• Production-Ready: FastAPI, Docker, comprehensive testing, observability
• Cost Optimization: Save 40-60% by routing simple queries to direct inference

Quick Start

Prerequisites

• Python 3.11+
• Docker & Docker Compose
• Anthropic API key

Installation

# Clone the repository
git clone https://github.com/CrashBytes/tutorial-production-ai-reasoning.git
cd tutorial-production-ai-reasoning

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY

Running Locally

# Start the API server
uvicorn app.main:app --reload

# API will be available at http://localhost:8000
# API docs at http://localhost:8000/docs

Usage Examples

Basic Reasoning Request

import requests

response = requests.post(
    "http://localhost:8000/api/v1/reason",
    json={
        "prompt": "Calculate the compound interest on $10,000 at 5% annual rate for 3 years",
        "strategy": "auto"  # Automatically selects best strategy
    }
)

result = response.json()
print(f"Strategy used: {result['result']['strategy_used']}")
print(f"Cost: ${result['result']['cost_usd']:.4f}")
print(f"Answer: {result['result']['final_answer']}")

Reasoning Strategies

Strategy	Cost	Time	Use When
Direct	$0.002	800ms	Simple queries, classification, fact retrieval
Chain-of-Thought	$0.015	4.5s	Complex multi-step problems, analysis
Tree-of-Thoughts	$0.045	12s	Critical decisions, exploring multiple approaches
Extended Thinking	$0.080	25s	Maximum quality, expert-level reasoning

License

MIT License

Support

• Tutorial Article: crashbytes.com
• CrashBytes Blog: crashbytes.com

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Last Updated: November 18, 2025