#LLM

Moving beyond single-prompt LLM limits. An in-depth guide on Multi-Agent Orchestration architectures, detailing task delegation, message buses, state machines, and building collaborative agent teams.

Overcoming the limitations of pure vector database search in RAG pipelines. An honest guide to implementing keyword-based BM25, combining results with Reciprocal Rank Fusion (RRF), and applying Cohere Rerank to boost context quality.

Three core patterns that show up repeatedly in AI agent systems—Tool Use, ReAct, and Chain of Thought—explained with real TypeScript code. Understanding these patterns makes agent design significantly clearer.

Beyond basic prompting — how to get reliably structured output from LLMs. System/user/assistant role design, few-shot examples, chain-of-thought, JSON mode, function calling, and building type-safe LLM responses with Zod and the AI SDK.

LLMs don't know what they weren't trained on. Here's how RAG fixes that — walking through the complete pipeline from document ingestion to chunking, embedding, vector storage, retrieval, and generation with real Python and TypeScript examples.

My first LLM API integration brought token cost explosions, latency issues, and hallucinations. Here's what I learned building real features.

ChatGPT answers questions. AI Agents plan, use tools, and complete tasks autonomously. Understanding this difference changes how you build with AI.

Asking AI to 'make a login page' gives garbage. Structured prompts with context, constraints, and examples produce production-ready code.

When ChatGPT first came out, developers were terrified. 'Coding is dead.' I was scared too. But after integrating LLMs into production for a year, I realized: AI is not a God, but an incredibly smart intern who sometimes hallucinates confidently.

Both are children of Transformer, so why the difference? Using 'Fill-in-the-blank' vs 'Write-next-word' analogies to explain BERT vs GPT. Practical guide based on trial and error.

Understanding differences and selection criteria between fine-tuning and prompt engineering for LLM customization

My AI chatbot was hallucinating wild answers to customers. Here's how I implemented RAG (Retrieval-Augmented Generation) to fix it, covering Vector DBs, Embeddings, and Hybrid Search.