Category: Artificial Intelligence(AI)

Introduction: Not all queries are equal—some need fast, cheap responses while others require deep reasoning. Query routing intelligently directs requests to the right model, index, or processing pipeline based on query characteristics. Route simple factual questions to smaller models, complex reasoning to GPT-4, and domain-specific queries to specialized indexes. This approach optimizes both cost and […]

Introduction: LLM applications are notoriously hard to test. Outputs are non-deterministic, quality is subjective, and traditional unit testing doesn’t capture the nuances of language generation. Yet shipping untested LLM features is a recipe for embarrassing failures—hallucinations, off-brand responses, or security vulnerabilities. This guide covers practical testing strategies: deterministic unit tests for prompt templates, evaluation suites […]

Introduction: Context windows are the lifeblood of LLM applications—they determine how much information your model can process at once. Even with 128K+ token models, you’ll hit limits when dealing with long documents, conversation histories, or multi-document RAG. Poor context management leads to truncated information, lost context, and degraded responses. This guide covers practical strategies for […]

Introduction: Prompt injection is one of the most significant security risks in LLM applications. Attackers craft inputs that manipulate the model into ignoring its instructions, leaking system prompts, or performing unauthorized actions. As LLMs become more integrated into production systems—handling sensitive data, executing code, or making API calls—the attack surface grows dramatically. This guide covers […]

Introduction: Evaluating LLM outputs is fundamentally different from traditional ML metrics. You can’t just compute accuracy when there’s no single correct answer, and human evaluation doesn’t scale. This guide covers the full spectrum of LLM evaluation: automated metrics like BLEU, ROUGE, and BERTScore for measuring similarity; semantic metrics that capture meaning beyond surface-level matching; LLM-as-judge […]

Introduction: Vector databases are the backbone of modern AI applications—powering semantic search, RAG systems, and recommendation engines. But as your vector collection grows from thousands to millions of embeddings, naive approaches break down. Query latency spikes, memory costs explode, and recall accuracy degrades. This guide covers practical optimization strategies: choosing the right index type for […]