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#prompt mühendisliği

38 posts

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What Are the Differences Between Base Models, Instruction-Tuned Models, and Reasoning Models?

Three of the most commonly confused concepts in the LLM landscape are base models, instruction-tuned models, and reasoning models. Yet these model types differ significantly in how they are trained, how they respond to user instructions, how much guidance they need, what tasks they are best suited for, and how they should be positioned in enterprise systems. Base models behave primarily as raw next-token predictors, instruction-tuned models are aligned to follow user intent more effectively, and reasoning models are designed to spend more compute on complex, multi-step, and ambiguous tasks. This guide explains the differences across training logic, behavior, prompting style, latency-cost trade-offs, quality profile, and enterprise use cases.

26 min
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What to Do When Prompt Engineering Is Not Enough: When You Need Workflows, Retrieval, and Tool Use

Many organizations turn their first successful experiences with large language models into the mistaken belief that prompt engineering can solve every problem. In reality, while prompt design is a powerful starting point, not every task can be solved by writing better instructions. Multi-step processes require workflows, up-to-date and organization-specific knowledge requires retrieval, and interactions with systems, data sources, or business actions require tool use. This guide explains the limits of prompt engineering in enterprise settings, clarifies when prompting is enough, and shows when workflows, retrieval, or tool use become necessary—and how these layers should work together in production-grade systems.

26 min
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How to Measure Prompt Quality: An Evaluation Framework for Accuracy, Consistency, and Task Success

In enterprise AI systems, evaluating prompt quality through intuition alone is not enough. A prompt that “looks good” is not necessarily reliable in production. The real questions are whether the prompt produces correct outputs, behaves consistently across similar inputs, completes the intended task successfully, and can be monitored over time. This guide presents an enterprise evaluation framework for prompt quality covering accuracy, consistency, task success, schema compliance, uncertainty handling, human correction effort, cost, and regression tracking. The goal is to move prompt engineering from subjective preference into measurable quality management.

25 min
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blog-prompt-muhendisligi

Prompt Patterns: The Most Effective Templates for Extraction, Classification, Reasoning, Critique, and Planning

One of the most common mistakes in enterprise prompt engineering is trying to solve every task with the same style of instruction. In reality, task families such as extraction, classification, reasoning, critique, and planning require different prompt patterns, output structures, and quality control rules. Choosing the wrong pattern introduces ambiguity; choosing the right one enables more controlled, consistent, and measurable behavior from the same model. This guide explains the five most important prompt pattern families from an enterprise perspective, covering their design logic, template structure, common failure modes, evaluation criteria, and production-ready usage principles.

26 min