Author: Nithin Mohan TK

Introduction: LLM inference is expensive—both in compute and latency. Every token generated requires a forward pass through billions of parameters, and users expect responses in seconds, not minutes. Inference optimization techniques reduce costs and improve responsiveness without sacrificing output quality. This guide covers practical optimization strategies: batching requests to maximize GPU utilization, managing KV caches […]

Introduction: LLMs generate text, but applications need structured data—JSON objects, database records, API payloads. Getting reliable structured output from language models requires more than asking nicely in the prompt. This guide covers practical techniques for structured generation: defining schemas with Pydantic or JSON Schema, using constrained decoding to guarantee valid output, implementing retry logic with […]

Introduction: Not every request needs GPT-4. Simple questions can be handled by smaller, faster, cheaper models, while complex reasoning tasks benefit from more capable ones. Model routing intelligently directs requests to the most appropriate model based on task complexity, cost constraints, latency requirements, and quality needs. This approach can reduce costs by 50-80% while maintaining […]

Introduction: LLMs are stateless—each request starts fresh with no memory of previous interactions. Building conversational applications requires implementing memory systems that maintain context across turns while staying within token limits. The challenge is balancing completeness (keeping all relevant context) with efficiency (not wasting tokens on irrelevant history). This guide covers practical memory patterns: buffer memory […]

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 […]