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 →Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
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. […]
Read more →LLM Testing Strategies: Unit Tests, Evaluation Metrics, and Regression Testing
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 […]
Read more →Agent Memory Patterns: Building Persistent Context for AI Agents
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, […]
Read more →Structured Generation Techniques: Getting Reliable JSON from LLMs
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 […]
Read more →LLM Caching Strategies: Reducing Costs and Latency with Smart Response Caching
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 […]
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