I'm Luis Rasgado

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI applications that need access to custom knowledge bases. But there's a significant gap between a quick prototype and a production-ready system.

Key Lessons Learned

1. Chunking Strategy Matters - Don't just split on character count. Consider semantic boundaries, overlapping chunks, and metadata preservation.
2. Hybrid Search is King - Pure vector search has limitations. Combine with BM25 for keyword matching, metadata filters, and re-ranking.
3. Evaluation is Non-Negotiable - Build evaluation pipelines early with retrieval metrics (recall@k, MRR) and generation quality measures.

Building production RAG systems requires thinking beyond the happy path. Invest in evaluation, monitoring, and iteration.

AI agents are having their moment. But between the hype of "autonomous AI" and the reality of production systems, there's a lot of practical wisdom to share.

Patterns That Actually Work

1. ReAct (Reasoning + Acting) - The foundation of most agent architectures. Interleave thinking and action for better results.
2. Plan-and-Execute - For complex tasks, separate planning from execution. Generate a high-level plan first, then execute with ability to re-plan.
3. Multi-Agent Collaboration - Sometimes one agent isn't enough. Use specialized agents for research, analysis, and writing.

Common Pitfalls

• Over-autonomy: Don't let agents run forever
• Poor error handling: Agents will fail, plan for it
• Missing guardrails: Always validate outputs

When building AI-powered React applications, performance challenges multiply. Streaming responses, real-time updates, and complex visualizations can quickly overwhelm your UI.

Key Strategies

1. Virtualization - For long chat histories or document lists, use react-window or react-virtualized.
2. Streaming Optimization - Batch DOM updates during LLM streaming. Don't re-render on every token.
3. Web Workers - Offload heavy computations like embedding similarity calculations to background threads.
4. Memoization - Use React.memo, useMemo, and useCallback strategically, especially for components receiving streaming data.

The goal is maintaining 60fps while processing complex AI responses. It's achievable with the right architecture.