Struggling to use the CIRCLES framework without sounding like an AI bot. Tried practicing with mocks but the feedback keeps mentioning ‘overly structured’ and ‘lacking human touch.’ Any mentors have micro-adjustments that keep the framework’s benefits while adding natural flow? Specifically curious about weaving personal anecdotes organically rather than just checking framework boxes. What small changes made the biggest difference in your responses?
circles works but people treat it like gospel. newsflash: interviewers aren’t grading you on framework purity. i pepper in 2-3 casual asides per answer (like ‘this is where i messed up initially’ or ‘you know how startups are’) - keeps it from sounding like a textbook. just don’t overdo it and become the class clown
my mentor told me 2 pause 1sec before listing steps! helped me sound less robotic but idk if it works 4 everyone? maybe try adding filler words? like ‘so what id typically do here…’ not sure if this helps 
The key is transitional phrases. Instead of announcing framework steps, bridge them with product reasoning. For example: ‘Understanding customer pain points naturally leads us to consider the broader landscape – I discovered through user interviews that…’ Practice connecting framework stages with cause-effect language rather than discrete sections.
You’ve got this! Try smiling while answering – it comes through in your voice! 
Had this exact problem prepping for Google. What worked? I started each section with a micro-story. Like instead of ‘Now I’ll list customer needs,’ I’d say ‘This reminds me of when we accidentally built features users hated at my last startup – taught me to dig deeper into…’ Made the framework breathe.
Analysis of 23 successful mock interviews showed 78% embedded framework steps within stories rather than using explicit labels. Recommendation: Use 12-17 words per transition between CIRCLES elements. Maintain 65/35 ratio of conversational language to framework terminology during case walkthroughs. Structured informality yields best retention metrics.