Introduction: Multi-turn conversations are where LLM applications become truly useful. Users don’t just ask single questions—they refine, follow up, reference previous context, and expect the assistant to remember what was discussed. Building effective multi-turn systems requires careful attention to context management, history compression, turn-taking logic, and graceful handling of topic changes. This guide covers practical […]
Read more →LLM Output Parsing: Extracting Structured Data from Language Model Responses
Introduction: LLMs generate text, but applications need structured data. Parsing LLM outputs reliably is one of the most common challenges in production systems. The model might return JSON with extra text, miss required fields, use unexpected formats, or hallucinate invalid values. This guide covers practical parsing strategies: using structured output modes, building robust parsers with […]
Read more →Advanced Retrieval Strategies for RAG: From Query Transformation to Multi-Stage Pipelines
Introduction: Retrieval is the foundation of RAG systems. Poor retrieval means irrelevant context, which leads to hallucinations and wrong answers regardless of how capable your LLM is. Yet many RAG implementations use naive approaches—single-stage vector search with default settings. This guide covers advanced retrieval strategies: query transformation techniques, hybrid search combining dense and sparse methods, […]
Read more →LLM Application Monitoring: Metrics, Tracing, and Alerting for Production AI Systems
Introduction: LLM applications fail in ways traditional software doesn’t. A model might return syntactically correct but factually wrong responses. Latency can spike unpredictably. Costs can explode without warning. Token usage varies wildly based on input. Traditional APM tools miss these LLM-specific failure modes. This guide covers comprehensive monitoring for LLM applications: tracking latency, tokens, and […]
Read more →Prompt Injection Defense: Protecting LLM Applications from Adversarial Inputs
Introduction: Prompt injection is the SQL injection of the AI era. Attackers craft inputs that manipulate your LLM into ignoring instructions, leaking system prompts, or performing unauthorized actions. As LLMs gain access to tools, databases, and APIs, the attack surface expands dramatically. A successful injection could exfiltrate data, execute malicious code, or compromise your entire […]
Read more →LLM Model Selection: Choosing the Right Model for Every Task
Introduction: Choosing the right LLM for your task is one of the most impactful decisions you’ll make. Use a model that’s too small and you’ll get poor quality. Use one that’s too large and you’ll burn through budget while waiting for slow responses. The landscape changes constantly—new models launch monthly, pricing shifts, and capabilities evolve. […]
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