Category: Artificial Intelligence(AI)

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… Continue reading

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,… Continue reading

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… Continue reading

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… Continue reading

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.… Continue reading

Introduction: Testing LLM applications is fundamentally different from testing traditional software. Outputs are non-deterministic, quality is subjective, and edge cases are infinite. You can’t simply assert that output equals expected—you need to evaluate whether outputs are good enough across multiple dimensions. Yet many teams skip testing entirely or rely solely on manual spot-checking. This guide… Continue reading

Introduction: Memory is what transforms a stateless LLM into a persistent, context-aware agent. Without memory, every interaction starts from scratch—the agent forgets previous conversations, learned preferences, and accumulated knowledge. But implementing memory for agents is more complex than simply storing chat history. You need short-term memory for the current task, long-term memory for persistent knowledge,… Continue reading

Introduction: Getting LLMs to output valid JSON, XML, or other structured formats is surprisingly difficult. Models hallucinate extra fields, forget closing brackets, and produce malformed output that breaks downstream systems. Prompt engineering helps but doesn’t guarantee valid output. This guide covers techniques for reliable structured generation: using native JSON mode and structured outputs, constrained decoding… Continue reading

Introduction: LLM API calls are expensive and slow. A single GPT-4 request can cost $0.03-0.12 and take 2-10 seconds. When users ask similar questions repeatedly, you’re paying for the same computation over and over. Caching solves this by storing responses and returning them instantly for matching requests. But LLM caching is harder than traditional caching—users… Continue reading

Introduction: Choosing the right embedding model is critical for RAG systems, semantic search, and similarity applications. The wrong choice leads to poor retrieval quality, high costs, or unacceptable latency. OpenAI’s text-embedding-3-small is cheap and fast but may miss nuanced similarities. Cohere’s embed-v3 excels at multilingual content. Open-source models like BGE and E5 offer privacy and… Continue reading