Prompt Engineering for Builders
Prompt engineering for builders goes beyond basic instructions. This section covers the techniques that matter in production - getting consistent, reliable outputs; applying reasoning patterns that improve accuracy; and managing prompts at scale as living artifacts of your system.
In This Section
Getting Reliable LLM Outputs
Structured outputs, JSON mode, temperature control, grounding, and retry patterns - a builder's guide to predictable LLM responses.
Chain-of-Thought & Reasoning Prompts
How CoT, ReAct, and scratchpad prompting improve accuracy on complex tasks - and when they backfire.
Few-Shot, Zero-Shot & Many-Shot
When examples in the prompt help vs hurt, how to select and format good examples, and the many-shot advantage of long-context models.
Meta-Prompting & Self-Consistency
Self-refinement, majority voting, self-consistency sampling, and using the model to improve its own outputs.
Prompt Versioning & Management
Treating prompts as code - version control, A/B testing, prompt registries, and evaluation cadences that catch regressions.