LLM Context Windows: Trade-offs Beyond Token Count
Why bigger context windows are not always better: cost, attention degradation, retrieval design, and how to architect for long-context tasks.
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Why bigger context windows are not always better: cost, attention degradation, retrieval design, and how to architect for long-context tasks.
A field guide to the most common prompt engineering anti-patterns, why they degrade LLM output quality, and concrete refactors that fix each one.
Use chain-of-thought prompting to unlock multi-step reasoning, with zero-shot, few-shot, and structured variants for production use.
How to build evaluation loops for prompts so you can iterate with evidence instead of vibes. Covers datasets, graders, regressions, and how to make eval cheap enough to run often.
Decide between zero-shot and few-shot prompting by weighing example quality, cost, and how strictly you need to control output format.
How to coax LLMs into producing predictable, parseable output using output formatters, JSON schemas, examples, and validation loops that actually hold up in production code paths.
Learn the ReAct pattern, a prompting technique that combines reasoning and action to build effective tool-using LLM agents.
Learn how self-consistency prompting samples multiple reasoning paths and aggregates answers to improve accuracy, with hands-on examples and trade-offs.
Write system prompts that steer model behavior reliably: role, format, constraints, refusals, and evaluation patterns that actually work.
Practical prompt engineering for building software with LLMs: structure, few-shot, chain-of-thought, role messages, and what actually moves quality.
Explore Tree of Thought prompting, which lets LLMs branch, evaluate, and backtrack through reasoning steps to solve problems chain-of-thought cannot.
How to write prompts and tool definitions that make function calling reliable. Covers schemas, descriptions, examples, error handling, and patterns for multi-tool agents.