How to Reduce LLM API Costs Without Hurting Quality
Caching, model routing, prompt trimming and batching explained in plain English, with when to use each technique and how to cut your LLM API bill without making answers worse.
Background instructions given to a language model by the developer that set its role, tone, rules, and limits for every conversation.
A system prompt is the part of a model's input that users usually do not see. Developers use it to describe who the assistant is, what it should and should not do, which tools it can use, and how answers should be formatted. User messages then follow on top of these instructions.
For example, a restaurant's booking assistant might have a system prompt stating that it only handles reservations, must confirm party size and time, should never promise tables that are not available, and should hand complaints to staff. A clear system prompt makes behaviour more consistent, but it is not a security boundary. Users can sometimes coax a model into revealing or ignoring its instructions, so sensitive rules should also be enforced in the application code.
Rules, filters, and checks placed around an AI system to keep its inputs, outputs, and actions within safe and acceptable limits.
Prompt EngineeringThe practice of designing and refining instructions given to AI models to get more accurate, useful, and reliable outputs.
Prompt InjectionAn attack that hides instructions in text an AI system reads, tricking it into ignoring its original rules or taking unintended actions.
Caching, model routing, prompt trimming and batching explained in plain English, with when to use each technique and how to cut your LLM API bill without making answers worse.
How to build a small test set and scoring rubric before you ship an AI feature, which grading methods to use, and how to keep quality from slipping when you change prompts or models.
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