Recent Posts
LLM Observability: Tracing, Logging, and Debugging AI Applications
March 12, 2026
Learn how to implement proper observability for LLM applications, including prompt tracing, cost tracking, latency monitoring, and debugging with tools like LangSmith and LangFuse.
Hybrid Search: Combining Keyword and Vector Search for Better Retrieval
March 05, 2026
Learn why production RAG systems combine keyword-based BM25 and vector embeddings for superior retrieval...
PEFT Methods Explained: LoRA, QLoRA, and Adapter-Based Fine-Tuning
February 27, 2026
Learn how parameter-efficient fine-tuning (PEFT) methods like LoRA and QLoRA allow you to customize...
Why Your LLM Application Feels Slow
February 22, 2026
Production LLM applications often feel slow not because of model limitations but due to...
All Articles
A Beginner’s Guide to Cost Optimization in LLM Applications
February 20, 2026
A complete beginner-friendly guide to optimizing costs in large language model applications, including model selection, prompt...
How Retrieval-Augmented Generation (RAG) Works
February 19, 2026
Understanding how Retrieval-Augmented Generation works and why it is important for modern AI applications.
What is an AI Agent?
February 18, 2026
A detailed breakdown of what LLM agents really are, how they work internally, and the architecture...
Navigating the 3 Critical Hurdles of Multimodal AI Agent Deployment
February 17, 2026
Deploying multimodal AI agents is not just about feeding images into an LLM. This post breaks...
Multimodal AI and Grounding Challenges
February 16, 2026
Explore the biggest grounding challenges in multimodal AI, including visual hallucinations, weak spatial reasoning, dataset bias,...
Context Window Limits: Why Your LLM Still Hallucinates
February 13, 2026
Learn why LLMs hallucinate even with large context windows, how token limits impact reasoning, and what...
