Prompt engineering designs and refines instructions and contextual information so AI models produce more accurate, relevant, and useful outputs in web applications. It replaces generic prompts with deliberate guidance matched to user and application needs. section 5.0
Use prompt engineering through complementary strategies:
- Few-shot learning supplies a limited number of examples so models can adapt quickly to new tasks. section 5.0
- Chain-of-thought prompting encourages step-by-step reasoning by breaking complex problems into manageable components. section 5.6
- Control tokens, message prompts, and system prompts define behavioral rules and interaction-specific instructions. section 5.6
- Template-based generation, function calling, schema validation, parsing, reprompting, and postprocessing improve the reliability of structured outputs. section 4.3.2
- Prompt management stores prompts for dynamic updates, version control, scalability, access control, and evaluation of output changes. section 5.6
Structured interview feedback
An interview assistant can use tailored prompt engineering to produce feedback or reviews focused on specific needs and areas for improvement, rather than generic responses. section 10.5
