Category: Technology Engineering

Introduction: LLMs can generate harmful, biased, or inappropriate content. They can be manipulated through prompt injection, jailbreaks, and adversarial inputs. Production applications need guardrails—safety mechanisms that validate inputs, moderate content, and filter outputs before they reach users. This guide covers practical guardrail implementations: input validation to catch malicious prompts, content moderation using classifiers and LLM-based […]

Introduction: Semantic search goes beyond keyword matching to understand the meaning and intent behind queries. By converting text to dense vector embeddings, semantic search finds conceptually similar content even when exact words don’t match. However, naive implementations often underperform—poor embedding choices, suboptimal indexing, and lack of reranking lead to irrelevant results. This guide covers practical […]

Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost cents and take seconds—multiply that by thousands of users and costs spiral quickly. Caching is the most effective way to reduce both cost and latency. But LLM caching is different from traditional caching: exact string matches are rare, and semantically similar […]

Introduction: Context windows are limited and tokens are expensive. Long prompts with extensive context, examples, or retrieved documents quickly hit limits and drive up costs. Prompt compression techniques reduce token count while preserving the information LLMs need to generate quality responses. This guide covers practical compression strategies: token pruning to remove low-information tokens, extractive summarization […]

Introduction: RAG systems live or die by their chunking strategy. Chunk too large and you waste context window space with irrelevant content. Chunk too small and you lose semantic coherence, making it hard for the LLM to understand context. The right chunking strategy depends on your document types, query patterns, and retrieval approach. This guide […]

Introduction: LLM APIs are inherently slow—even fast models take hundreds of milliseconds per request. When you need to process multiple prompts, make parallel API calls, or handle high-throughput workloads, synchronous code becomes a bottleneck. Async patterns let you overlap I/O wait times, dramatically improving throughput without adding complexity. This guide covers practical async patterns for […]