Safety, Testing & Production
Prompt injection defense, testing strategies, and production deployment.
A/B Testing and Prompt Experiments
A/B testing prompts applies controlled experimentation to compare prompt variants with real users, measuring causal impact on task success, user satisfaction, and cost through statistically rigorous traffic splitting. Prerequisites: prompt-testing-and-evaluation.md, prompt-optimization-techniques.md.
Cost and Latency Optimization
Cost and latency optimization for LLM applications involves systematic techniques — prompt compression, caching, model routing, and batching — to find the best trade-off on the cost-quality Pareto frontier. Prerequisites: prompt-optimization-techniques.md, prompt-testing-and-evaluation.md, 06-context-engineering-fundamentals/context-budget-allocation.md.
Guardrails and Output Filtering
Guardrails are programmable safety layers that inspect, validate, and filter LLM outputs before they reach the user, functioning as quality control inspectors at the end of a production line. Prerequisites: prompt-injection-defense-techniques.md, 05-structured-output-and-format-control/json-mode-and-schema-enforcement.md.
Prompt Debugging and Failure Analysis
Prompt debugging systematically identifies why an LLM produces incorrect or unexpected outputs by reproducing failures, isolating causal components, and verifying fixes — applying the same disciplined methodology used to debug software. Prerequisites: prompt-testing-and-evaluation.md, prompt-optimization-techniques.md, 04-system-prompts-and-instruction-design/system-prompt-anatomy.md.
Prompt Injection Defense Techniques
Prompt injection attacks attempt to override or subvert an LLM’s intended instructions, and defending against them requires layered security strategies spanning input sanitization, architectural isolation, and runtime detection. Prerequisites: 04-system-prompts-and-instruction-design/system-prompt-anatomy.md, 06-context-engineering-fundamentals/context-budget-allocation.md.
Prompt Optimization Techniques
Prompt optimization uses systematic methods — ablation studies, component analysis, and automated tuning — to improve prompt performance, analogous to tuning a recipe by changing one ingredient at a time. Prerequisites: prompt-testing-and-evaluation.md, 02-core-prompting-techniques/few-shot-prompting.md, 04-system-prompts-and-instruction-design/system-prompt-anatomy.md.
Prompt Testing and Evaluation
Prompt evaluation uses structured test datasets, automated scoring methods, and regression testing to systematically measure prompt quality — treating prompts with the same rigor as software code. Prerequisites: 02-core-prompting-techniques/few-shot-prompting.md, 05-structured-output-and-format-control/json-mode-and-schema-enforcement.md.
Red-Teaming Prompts
Red-teaming is systematic adversarial testing of LLM applications — hiring a locksmith to test your locks — using structured attack taxonomies, human creativity, and automated tools to discover vulnerabilities before real attackers do. Prerequisites: prompt-injection-defense-techniques.md, guardrails-and-output-filtering.md, prompt-testing-and-evaluation.md.