Introduction: Evaluating LLM outputs is fundamentally different from traditional ML evaluation. There’s no single ground truth for creative tasks, quality is subjective, and outputs vary with each generation. Yet rigorous evaluation is essential for production systems—you need to know if your prompts are working, if model changes improve quality, and if your system meets user […]
Read more →Category: Technology Engineering
Technology Engineering
Text-to-SQL with LLMs: Building Natural Language Database Interfaces
Introduction: Natural language to SQL is one of the most practical LLM applications. Business users can query databases without knowing SQL, analysts can explore data faster, and developers can prototype queries quickly. But naive implementations fail spectacularly—generating invalid SQL, hallucinating table names, or producing queries that return wrong results. This guide covers building robust text-to-SQL […]
Read more →Context Distillation Methods: Extracting Signal from Long Documents
Introduction: Long contexts contain valuable information, but they also contain noise, redundancy, and irrelevant details that consume tokens and dilute model attention. Context distillation extracts the essential information from lengthy documents, conversations, or retrieved passages, producing compact representations that preserve what matters while discarding what doesn’t. This technique is crucial for RAG systems processing multiple […]
Read more →Query Routing: Intelligent Request Distribution for Cost-Efficient AI Systems
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 […]
Read more →Knowledge Distillation: Transferring Intelligence from Large to Small Models
Introduction: Knowledge distillation transfers the capabilities of large, expensive models into smaller, faster ones that can run efficiently in production. Instead of training a small model from scratch, distillation leverages the “dark knowledge” encoded in a teacher model’s soft probability distributions—information that hard labels alone cannot capture. This guide covers the techniques that make distillation […]
Read more →Conversation State Management: Building Context-Aware AI Assistants
Introduction: Conversation state management is the foundation of building coherent, context-aware AI assistants. Without proper state management, every message is processed in isolation—the assistant forgets what was discussed moments ago, loses track of user preferences, and fails to maintain the thread of complex multi-turn conversations. Effective state management involves storing conversation history, extracting and persisting […]
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