Many question whether writing prompts for large language models (LLMs) like Claude and ChatGPT is just busy work.
It’s a crucial skill where where developers and linguists can collaborate to fully leverage the potential of LLMs.
Unlike code, prompts aren’t static—they require ongoing attention, collaboration, and constant re-evaluation.
Beyond Writing Prompts
- People often expect LLM prompts to behave like code, producing consistent outcomes.
- However, LLMs are dynamic, continually learning from new data, languages, and updates.
- So, writing a prompt isn’t a one-and-done task. “Set it and forget it” doesn’t apply to prompts. Regular sanity checks ensure prompts remain effective as LLMs evolve
- After LLM updates, prompts often need revisiting. We’ve seen finely tuned prompts lose effectiveness following model changes, reinforcing the need for ongoing prompt management.
- To ensure accuracy and reliability, we’ve developed automated pipelines and quality evaluation systems that constantly test and monitor prompt performance.
- Let’s be clear: Automation isn’t enough. Automation helps streamline prompt evaluations, but human expertise remains essential. Skilled human evaluators analyze and fine-tune outputs, adding the human expertise necessary for optimal results.
- Combine ongoing automated evaluations with human-in-the-loop systems to ensuring that skilled professionals continually fine-tune and maintain the quality of AI outputs.
To truly trust AI-generated outputs, we need to be in control of the process.
That’s why our role goes beyond prompt writing.
We focus on engineering and managing prompts, performing regression tests, automating evaluations
