20 posts · page 1 of 1
Master chain-of-thought prompting to improve LLM accuracy on complex tasks through step-by-step reasoning, zero-shot CoT, and structured thinking patterns.
Learn how to measure prompt quality with evaluation datasets, scoring rubrics, A/B testing, and automated grading to iterate on prompts with evidence.
Master few-shot prompting to teach LLMs new tasks through carefully selected examples, formatting patterns, and example ordering strategies.
Learn prompt patterns for writing, reviewing, debugging, and refactoring code with LLMs, including practical templates and real examples.
Learn how to design multi-turn LLM conversations with effective context management, memory patterns, conversation state tracking, and production architectures.
Learn how to craft system prompts that reliably control LLM behavior through persona setting, constraints, output rules, and guardrails.
Learn how to reliably extract JSON, XML, tables, and other structured formats from LLMs using schema enforcement, prompt patterns, and validation.
Understand how temperature, top-p, max tokens, frequency penalty, and other LLM parameters affect output quality, and learn how to tune them for different tasks.
Learn how to break complex AI tasks into sequential prompt chains that improve accuracy, debuggability, and output quality.
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.
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.
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.
Decide between zero-shot and few-shot prompting by weighing example quality, cost, and how strictly you need to control output format.
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.