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. […]
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Prompt Templates and Management: Building Maintainable LLM Applications
Introduction: As LLM applications grow in complexity, managing prompts becomes a significant engineering challenge. Hard-coded prompts scattered across your codebase make iteration difficult, A/B testing impossible, and debugging a nightmare. Prompt template management solves this by treating prompts as first-class configuration—versioned, validated, and dynamically rendered. A good template system separates prompt logic from application code, […]
Read more →Prompt Debugging Techniques: Systematic Approaches to Fixing LLM Failures
Introduction: Prompt debugging is an essential skill for building reliable LLM applications. When prompts fail—producing incorrect outputs, hallucinations, or inconsistent results—systematic debugging techniques help identify and fix the root cause. Unlike traditional software debugging where you can step through code, prompt debugging requires understanding how language models interpret instructions and where they commonly fail. This […]
Read more →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 →Knowledge Graph Integration: Structured Reasoning for LLM Applications
Introduction: Vector search finds semantically similar content, but it misses the structured relationships that make knowledge truly useful. Knowledge graphs capture entities and their relationships explicitly—who works where, what depends on what, how concepts connect. Combining knowledge graphs with LLMs creates systems that can reason over structured relationships while generating natural language responses. This guide […]
Read more →Fine-Tuning LLMs: From Data Preparation to Production Deployment
Introduction: Fine-tuning transforms a general-purpose LLM into a specialized model tailored to your domain, style, or task. While prompt engineering can get you far, fine-tuning offers consistent behavior, reduced token usage, and capabilities that prompting alone cannot achieve. This guide covers the complete fine-tuning workflow—from data preparation to deployment—using both cloud APIs (OpenAI, Together AI) […]
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