Introduction: Prompt engineering has emerged as one of the most critical skills in the AI era. The difference between a mediocre AI response and an exceptional one often comes down to how you structure your prompt. After years of working with large language models across production systems, I’ve distilled the most effective techniques into this […]
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LLM Caching Strategies: From Exact Match to Semantic Similarity
Introduction: LLM API calls are expensive and slow. Caching is your first line of defense against runaway costs and latency. But caching LLM responses isn’t straightforward—the same question phrased differently should return the same cached answer. This guide covers caching strategies for LLM applications: exact match caching for deterministic queries, semantic caching using embeddings for […]
Read more →LLM Memory and Context Management: Building Conversational AI That Remembers
Introduction: LLMs have no inherent memory—each API call is stateless. The model doesn’t remember your previous conversation, your user’s preferences, or the context you established five messages ago. Memory is something you build on top. This guide covers implementing different memory strategies for LLM applications: buffer memory for recent context, summary memory for long conversations, […]
Read more →OpenAI API Complete Guide: From Chat Completions to Assistants
A comprehensive guide to the OpenAI API covering GPT-4o, function calling, the Assistants API, vision capabilities, and production best practices with code examples.
Read more →LLM Application Logging and Tracing: Building Observable AI Systems
Introduction: Production LLM applications require comprehensive logging and tracing to debug issues, monitor performance, and understand user interactions. Unlike traditional applications, LLM systems have unique logging needs: capturing prompts and responses, tracking token usage, measuring latency across chains, and correlating requests through multi-step workflows. This guide covers practical logging patterns: structured request/response logging, distributed tracing […]
Read more →Guardrails and Safety for LLMs: Building Secure AI Applications with Input Validation and Output Filtering
Introduction: Production LLM applications need guardrails to ensure safe, appropriate outputs. Without proper safeguards, models can generate harmful content, leak sensitive information, or produce responses that violate business policies. Guardrails provide defense-in-depth: input validation catches problematic requests before they reach the model, output filtering ensures responses meet safety standards, and content moderation prevents harmful generations. […]
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