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 […]
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Knowledge Graphs with LLMs: Building Structured Knowledge from Text
Introduction: Knowledge graphs represent information as entities and relationships, enabling powerful reasoning and querying capabilities. LLMs excel at extracting structured knowledge from unstructured text—identifying entities, relationships, and attributes that can be stored in graph databases. This guide covers building knowledge graphs with LLMs: entity and relation extraction, graph schema design, populating Neo4j and other graph […]
Read more →Ollama: The Complete Guide to Running Open Source LLMs Locally
Introduction: Ollama has revolutionized how developers run large language models locally. With a simple command-line interface and seamless hardware acceleration, you can have Llama 3.2, Mistral, or CodeLlama running on your laptop in minutes—no cloud API keys, no usage costs, complete privacy. Built on llama.cpp, Ollama abstracts away the complexity of model quantization, memory management, […]
Read more →LLM Output Parsing: From Raw Text to Typed Objects
Introduction: LLMs generate text, but applications need structured data. Parsing LLM output reliably is surprisingly tricky—models don’t always follow instructions, JSON can be malformed, and edge cases abound. This guide covers robust output parsing strategies: using JSON mode for guaranteed valid JSON, Pydantic for type-safe parsing, handling partial and streaming outputs, implementing retry logic for […]
Read more →Conversation State Management: Context Tracking, Slot Filling, and Dialog Flow
Introduction: Conversational AI applications need to track state across turns—remembering what users said, what information has been collected, and where they are in multi-step workflows. Unlike simple Q&A, task-oriented conversations require slot filling, context tracking, and flow control. This guide covers practical state management patterns: conversation context objects, slot-based information extraction, finite state machines for […]
Read more →GPU Resource Management in Cloud: Optimizing AI Workloads
GPU resource management is critical for cost-effective AI workloads. After managing GPU resources for 40+ AI projects, I’ve learned what works. Here’s the complete guide to optimizing GPU resources in the cloud. Figure 1: GPU Resource Management Architecture Why GPU Resource Management Matters GPU resources are expensive and limited: Cost: GPUs are the most expensive […]
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