C4: Container, Code, Cloud & Context

Introduction: Prompt debugging is an essential skill for building reliable LLM applications. When prompts fail—producing incorrect outputs, hallucinations, or inconsistent results—systematic debugging techniques help identify and fix the root cause. Unlike traditional software debugging where you can step through code, prompt debugging requires understanding how language models interpret instructions and where they commonly fail. This […]

Introduction: Batch inference optimization is critical for cost-effective LLM deployment at scale. Processing requests individually wastes GPU resources—the model loads weights once but processes only a single sequence. Batching multiple requests together amortizes this overhead, dramatically improving throughput and reducing per-request costs. This guide covers the techniques that make batch inference efficient: dynamic batching strategies, […]

Introduction: LLM monitoring is essential for maintaining reliable, cost-effective AI applications in production. Unlike traditional software where errors are obvious, LLM failures can be subtle—degraded output quality, increased hallucinations, or slowly rising costs that go unnoticed until the monthly bill arrives. Effective monitoring tracks latency, token usage, error rates, output quality, and cost metrics in […]

Introduction: Understanding embedding spaces is crucial for building effective semantic search, RAG systems, and recommendation engines. Embeddings map text, images, or other data into high-dimensional vector spaces where similar items cluster together. But how do you know if your embeddings are working well? How do you debug retrieval failures or understand why certain queries return […]