Introduction: Tokens are the currency of LLM applications—every token costs money and consumes context window space. Efficient token usage directly impacts both cost and capability. This guide covers practical token optimization techniques: accurate token counting across different models, content compression strategies that preserve meaning, budget management for staying within limits, and prompt engineering patterns that […]
Read more →Category: Technology Engineering
Technology Engineering
Prompt Versioning and Management: Bringing Software Engineering Rigor to LLM Development
Introduction: Prompts are code. They determine how your LLM application behaves, and like code, they need version control, testing, and deployment pipelines. Yet many teams treat prompts as afterthoughts—hardcoded strings scattered across the codebase, changed ad-hoc without tracking. This leads to regressions, inconsistent behavior, and difficulty understanding why outputs changed. This guide covers practical prompt […]
Read more →Chain-of-Thought Prompting: Unlocking LLM Reasoning with Step-by-Step Thinking
Introduction: Chain-of-thought (CoT) prompting dramatically improves LLM performance on complex reasoning tasks. Instead of asking for a direct answer, you prompt the model to show its reasoning step by step. This simple technique can boost accuracy on math problems from 17% to 78%, and similar gains appear across logical reasoning, code generation, and multi-step analysis. […]
Read more →Prompt Compression: Fitting More Context into Your Token Budget
Introduction: Context windows are precious real estate. Every token you spend on context is a token you can’t use for output or additional information. Long prompts hit token limits, increase latency, and cost more money. Prompt compression techniques help you fit more information into less space without losing the signal that matters. This guide covers […]
Read more →Prompt Compression Techniques: Fitting More Context in Less Tokens
Introduction: Context windows are limited and tokens are expensive. Long prompts with extensive context, examples, or retrieved documents quickly hit limits and drive up costs. Prompt compression techniques reduce token count while preserving the information LLMs need to generate quality responses. This guide covers practical compression strategies: token pruning to remove low-information tokens, extractive summarization […]
Read more →Prompt Engineering Best Practices: From Basic Techniques to Advanced Reasoning Patterns
Introduction: Prompt engineering is the art and science of communicating effectively with large language models. Unlike traditional programming where you write explicit instructions, prompt engineering requires understanding how models interpret language, what context they need, and how to structure requests for optimal results. This guide covers the fundamental techniques that separate amateur prompts from production-quality […]
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