building-with-claude-api

# Building with Claude API 🚀 This repository documents my journey from learning the Anthropic Claude API to building **production-style AI systems**. Instead of treating this as a tutorial dump, I approached it like an engineer: * Every concept is implemented as a **working system** * Projects are structured like **real-world applications** * Emphasis is placed on **evaluation, scalability, and design patterns** --- ## 🧠 What This Repo Demonstrates This repo goes beyond basic API usage and focuses on **building intelligent systems** using Claude: * Prompt engineering & structured outputs * Multi-turn conversational systems * Retrieval-Augmented Generation (RAG) pipelines * Tool use & function calling * Model Context Protocol (MCP) implementations * Agent-based architectures (planning, routing, execution) * Evaluation frameworks for LLM outputs * Multimodal workflows (text, images, PDFs) --- ## 📂 Key Projects ### 🗣️ Conversational AI System A multi-turn chat system with context handling, system prompts, and response control. ### 🧪 Prompt Engineering + Evals Experimentation with prompt strategies, including: * Few-shot learning * XML structuring * Automated evaluation pipelines ### 📚 RAG Pipeline End-to-end retrieval system: * Document ingestion & chunking * Hybrid search (BM25 + embeddings) * Context-aware generation ### 🛠️ Tool-Using Agents Extending Claude with external tools and structured workflows. ### 🔌 MCP Server & Client Implementation of Model Context Protocol for standardized tool integration. ### 🤖 Agent Architectures Systems that simulate reasoning workflows: * Task routing * Parallel execution * Chained decision-making ### 🖼️ Multimodal Processing Using Claude for: * Image understanding * PDF parsing * Citation generation --- ## 🧪 Evaluation First Mindset A key focus of this repo is **measuring LLM performance**, not just building demos. Includes: * Test dataset generation * Automated grading pipelines * Prompt comparison workflows --- ## 🏗️ Design Philosophy This repository is built with the following principles: * **Systems over scripts** * **Evaluation over intuition** * **Modularity over monoliths** * **Real-world relevance over toy examples** --- ## 🛠️ Tech Stack * Python * Anthropic Claude API * Vector search / retrieval techniques * JSON-based structured outputs --- ## 🚀 Why This Repo Exists As a software engineer transitioning deeper into AI/ML systems, I wanted to: * Move beyond theory into **hands-on system design** * Understand how LLMs behave in **production-like environments** * Build a portfolio that reflects **real engineering depth** --- ## 📌 Next Steps * Scaling RAG systems with larger datasets * Improving evaluation pipelines * Exploring latency and cost optimization * Integrating with real-world applications --- ## 🤝 Connect If you're working on similar problems or exploring LLM systems, I'd love to connect and exchange ideas. ## 📄 License This project is licensed under the MIT License. ## 🙏 Acknowledgements This repository is based on a Claude API course by Anthropic. Some code patterns and exercises are adapted from the course material, and extended with my own implementations, experiments, and system designs.