Building 5 Production-Ready LLM Applications

Over the past few weeks, I embarked on an ambitious project to build 5 production-ready LLM applications based on concepts from "Building LLMs for Production" by Louis-François Bouchard. This blog series covers each project in detail, showcasing modern AI engineering practices and real-world implementations.

🎯 The Portfolio

I created five distinct applications, each demonstrating different aspects of LLM engineering:

1. 📰 AI News Summarizer

Real-time news aggregation with intelligent summarization

A production-grade system that fetches news from multiple sources (NewsAPI, RSS feeds) and generates concise summaries using GPT-4. Features include sentiment analysis, automatic categorization, and a REST API with background processing.

• Tech Stack: FastAPI, LangChain, GPT-3.5/4, SQLite, Redis
• Highlights: Background tasks, caching strategy, sentiment analysis
• Read the full post →

2. 🎥 YouTube Video Summarizer

AI-powered video transcription and analysis

Extract insights from YouTube videos using OpenAI's Whisper for transcription and GPT for multi-level summarization. Supports 90+ languages and generates timestamped key points.

• Tech Stack: OpenAI Whisper, yt-dlp, LangChain, FFmpeg
• Highlights: Audio processing pipeline, multi-level summaries, CLI + API
• Read the full post →

3. 🔍 Advanced RAG System

Production-ready Retrieval-Augmented Generation

A sophisticated Q&A system with hybrid search, automatic evaluation using RAGAS metrics, and support for multiple document types. Features query optimization and re-ranking.

• Tech Stack: LangChain, ChromaDB, Sentence Transformers, RAGAS
• Highlights: Hybrid retrieval, evaluation framework, document ingestion
• Read the full post →

4. 🧠 Knowledge Graph Generator

Transform text into visual knowledge graphs

Automatically extract entities and relationships from unstructured text using spaCy and GPT, then visualize them as interactive graphs. Supports Neo4j for advanced graph queries.

• Tech Stack: spaCy, Neo4j, NetworkX, Pyvis, D3.js
• Highlights: Entity extraction, relationship detection, interactive visualization
• Read the full post →

5. 🤖 AI Research Agent

Autonomous research and report generation

An autonomous agent that conducts multi-step research using the ReAct pattern, searches the web, and generates comprehensive reports with citations.

• Tech Stack: LangChain Agents, DuckDuckGo Search, BeautifulSoup
• Highlights: ReAct pattern, tool usage, autonomous planning
• Read the full post →

📊 What I Learned

Technical Skills Gained

1. LLM Integration Patterns

   • Prompt engineering and optimization
   • Structured output parsing
   • Cost management and token tracking
   • Error handling for LLM calls

2. Vector Databases

   • Embedding strategies
   • Hybrid search implementations
   • Chunking and retrieval optimization

3. Agent Architectures

   • ReAct pattern implementation
   • Tool usage and selection
   • Multi-step reasoning
   • Self-critique and refinement

4. Production Best Practices

   • Async processing
   • Background tasks
   • Proper logging and monitoring
   • Configuration management
   • Docker containerization

Architecture Insights

Each project follows production-ready principles:

• Modularity: Clear separation of concerns
• Scalability: Async operations, caching
• Observability: Comprehensive logging, health checks
• Security: Environment variables, input validation
• Documentation: Detailed READMEs, API docs

🛠️ The Tech Stack

All projects share a common foundation:

# Core Stack
- Python 3.9+
- LangChain (LLM framework)
- OpenAI API (GPT-3.5/4, Whisper)
- FastAPI (REST APIs)
- Pydantic (data validation)

Specialized technologies per project:

• Vector Databases: ChromaDB, Pinecone
• Graph Databases: Neo4j
• NLP: spaCy, NLTK, TextBlob
• Media Processing: yt-dlp, FFmpeg
• Web Scraping: BeautifulSoup, aiohttp
• Visualization: Pyvis, NetworkX, D3.js

📈 Key Metrics

• Total Lines of Code: ~3,500+
• Python Files: 33
• API Endpoints: 20+
• Git Repositories: 5
• Documentation Files: 6

💡 Challenges & Solutions

Challenge 1: Managing LLM Costs

Solution: Implemented caching strategies, token tracking, and fallback to smaller models for non-critical tasks.

Challenge 2: Handling Long Documents

Solution: Smart chunking with overlap, hierarchical summarization, and map-reduce patterns.

Challenge 3: Ensuring Response Quality

Solution: Evaluation frameworks (RAGAS), self-critique patterns, and structured output parsing.

Challenge 4: Production Reliability

Solution: Comprehensive error handling, retry logic, rate limiting, and proper logging.

🎓 Resources Used

This portfolio was inspired by:

1. "Building LLMs for Production" by Louis-François Bouchard
2. LangChain Documentation - Comprehensive guides and examples
3. OpenAI Cookbook - Best practices and patterns
4. Industry Blogs - Real-world implementations and case studies

🚀 What's Next?

Future improvements I'm planning:

1. Frontend Interfaces: React/Next.js UIs for each project
2. Cloud Deployment: Deploy all 5 projects to production
3. Testing Suite: Comprehensive unit and integration tests
4. Monitoring: Prometheus metrics and Grafana dashboards
5. Cost Optimization: Fine-tuned models, prompt caching

🔗 Explore the Projects

All projects are open source and available on GitHub:

• AI News Summarizer
• YouTube Video Summarizer
• Advanced RAG System
• Knowledge Graph Generator
• AI Research Agent

Each post includes:

• Complete architecture breakdown
• Code examples and explanations
• Challenges and solutions
• Performance metrics
• Deployment guide

💬 Let's Connect

I'd love to hear your thoughts on these projects! Feel free to:

• Comment below with questions or feedback
• Try out the projects and share your experience
• Suggest improvements or new features
• Connect with me on LinkedIn or Twitter

Ready to dive deeper? Start with the AI News Summarizer - it's a great introduction to the patterns used across all projects.

Stay tuned for detailed breakdowns of each project in the coming posts!