Technical deep-dive into implementing a production-ready RAG system with unified retrieval, smart query routing, and multi-provider LLM integration using FastAPI, ChromaDB, and async streaming architecture.
An honest take on using AI to build in unfamiliar programming languages - the benefits, risks, and finding the right balance between capability and responsibility.
Using AI like browser-first design: rapid prototyping, quick iteration, and refining later. A practical approach to AI as a creative tool.
How to enable and tune fuzzy matching in a RAG stack: thresholds, keyword fallbacks, and when to let semantic search lead.
A technical deep dive into distance vs. similarity thresholds, semantic post-filters, and calibrating fuzzy matching for search UX.
How input sanitization and query preprocessing protect your RAG system from security threats while improving performance. A technical deep-dive into the security validation pipeline that processes every user query.
A deep technical walkthrough of a production-grade RAG query router: intent analysis, confidence scoring, and robust fallback strategies with tunable thresholds.
A technical guide to choosing k in a production RAG stack, with code paths that honor runtime settings and expansion multipliers.
Why I quit my job to build a portfolio with bleeding-edge tech, and how your side projects can be strategic magnets for the exact opportunities you want.
Discover how Maximum Marginal Relevance (MMR) transforms RAG systems from repetitive answer machines into diverse, comprehensive knowledge assistants by balancing similarity with variety.
Master the balance between comprehensive context and focused responses in RAG systems by understanding context length limits, document counts, and fill ratios.
Learn how intelligent response caching can dramatically improve your RAG system's performance while maintaining answer quality and relevance.
Learn how safe deletion and vector store management prevent data disasters in RAG systems, ensuring your AI's knowledge base stays intact and reliable.
Discover how heading-aware document splitters preserve context and structure in RAG systems, turning fragmented text chunks into meaningful, searchable knowledge units.
A deep dive into score thresholds in RAG systems - what they are, how they work, and why getting them right makes the difference between helpful AI responses and confusing nonsense.