From Voice to Words: AI-Powered Message Variations for Job Hunting

AI-Powered Message Generator: When Your Voice Agent Needs to Talk About Job Hunting

Your voice agent project needed something every developer eventually faces: generating natural, contextual messages. In this case, the task was surprisingly human — crafting different ways to tell friends you’re looking for a new job.

The developer started with a straightforward approach: build a message generator that creates multiple variations of the same message, allowing users to pick the tone and style they prefer. This is where Claude’s API came in handy. Rather than hardcoding messages or creating rigid templates, the solution leveraged AI-powered generation to produce contextually appropriate variations.

The workflow was clean: the user provides the core intent (I’m job hunting and want to tell my friends), Claude generates several polished versions with different tones — maybe one casual, one professional, one humorous — and the system saves these options for later use. The developer initially wanted to persist these messages to a markdown file in the voice-agent project folder, recognizing that documentation and output management are crucial for voice agent systems.

This approach highlights an important pattern in modern AI development: augmentation over automation. Instead of the AI completely taking over the task, it provides multiple options that humans can review, choose from, and refine. It’s like having a writing assistant who doesn’t make final decisions but gives you solid material to work with.

One interesting tidbit about Claude’s design: it excels at tone adaptation because it was trained on diverse writing styles and learned to understand subtle shifts in voice. This makes it perfect for tasks like generating message variations. Tools like this are why ChatGPT’s earliest adopters quickly found it useful for email drafting and communication — it’s not just about generating text, it’s about generating appropriate text for different contexts.

The beauty of this solution lies in its flexibility. Whether your voice agent needs to generate job search messages, customer support responses, or appointment reminders, the same pattern applies: generate, review, pick, save. This human-in-the-loop approach prevents the common pitfall of fully autonomous systems producing output nobody actually wants to use.

The developer’s instinct to save outputs to a markdown file also reflects a best practice: keep your generated content accessible and versionable. Unlike a database blob, markdown files are human-readable, git-friendly, and easy to audit.

Documentation is like sex: when it’s good, it’s very good. When it’s bad, it’s better than nothing… 😄