Prompt Engineering16 min read
How to Design Efficient Prompts for Lower LLM Costs
Cut LLM API costs by 40–60% with concise, structured prompts—use delimiters, JSON/YAML, split tasks, set max_tokens, and iterate tests to reduce retries.
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Prompt Engineering
Browse all articles in the prompt engineering category.
Prompt Engineering12 min read
Why Feedback Loops Improve Prompt Quality
Systematic feedback loops—logging, evaluation, and human review—refine prompts to reduce errors, cut review costs, and accelerate quality improvements.
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Prompt Engineering12 min read
Prompt Versioning: Development vs. Production Best Practices
Treat prompts like code: apply semantic versioning, env-specific deployments, automated tests, canary rollouts, monitoring, and fast rollbacks to keep LLMs reliable.
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Prompt Engineering13 min read
How Prompt Versioning Prevents Editing Conflicts
Track every prompt change, prevent silent overwrites, enable safe testing, and rollback instantly with centralized prompt versioning for stable AI production.
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Prompt Engineering16 min read
Context Structuring Patterns for AI Teams
Structure prompts, RAG, CoT scaffolding, and pruning to cut hallucinations, lower token costs, and scale reliable AI team workflows.
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Prompt Engineering10 min read
Dynamic Prompts with {{Placeholders}}
Build reusable LLM prompts using {{placeholders}}—learn runtime variable passing, block-based templates, sanitization, and caching for scalable chatbots and RAG.
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Prompt Engineering10 min read
Why Block Order Matters in Prompts
The sequence of instructions, examples, and inputs can drastically change LLM outputs—test and optimize block order to improve reasoning and multimodal accuracy.
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