Prompt engineering
11 moments from 11 videos · 26:41 min total
Understand structural prompt design pattern techniques that ensure deterministic LLM behavior. These selected moments from expert presentations help engineers improve raw model reliability.
Prompt engineering techniques and security vulnerabilities
Providing targeted instructions and context eliminates unnecessary model complications but introduces potential security risks like malicious prompt injections.
Refining complex software instructions using automated prompt engineers
Using the model to refine initial phrasing and generate structured prompts for better results.
Mitigating context window limits with prompt engineering
Refining model inputs to produce targeted responses without relying on extensive fine-tuning.
Iterating on prompt engineering for edge cases
Advanced prompt modifications gracefully circumvent edge cases like outdated model knowledge and misleading developer comments.
Evolution from prompt engineering to harness engineering
Managing AI progressed from optimizing prompts and context windows to application-level harness engineering.
Applying extreme programming principles for mutual team benefit
Ensuring engineering practices provide immediate development advantages while securing long-term product stability.
Asking critical engineering questions to prevent software defects
Actively analyzing specifications, scalability, dependencies, and security during every stage builds quality before issues can emerge.
Navigating friction in spec-driven development and prompt engineering
Balancing dense markdown requirements with concise context helps avoid overwhelming developers and reduces cognitive load during code reviews.
Bypassing language model restrictions using creative prompt engineering
How requesting information in alternative formats like poetry can unintentionally subvert an AI model's safety guardrails.
Steering model behavior through effective prompt engineering techniques
Applying personas, structured outputs, and chain-of-thought methods effectively guides probabilistic models toward accurate code generation.
Shifting from prompt engineering to loop engineering
Moving beyond simple code generation towards orchestrating complex loops requires rethinking enterprise application development.
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