Anastasia C.

5 Claude facts most people don't talk about:

2. Trained to critique itself — using a set of written principles called Constitutional AI 3. Admits uncertainty — says "I don't know" instead of confidently lying to you 4. Analyzes full PDFs/documents in one shot — no copy-pasting needed 5. Has a consistent personality with actual opinions — it'll push back when you're wrong It's less of a tool, more of a thinking partner.

My pleasure 😊❤️

And Secondly, We treated the initial threshold as a v1 hypothesis, not a permanent rule, and made that explicit to Sales from day one. What worked in practice:

Every week we reviewed a small sample of sales-ready accounts with SDR managers: • Which converted to meetings • Which stalled • Which reps felt were “noise” That kept iteration grounded in outcomes, not opinions. 2. Adjust weights, not definitions We rarely changed what counted as intent. Instead, we tuned: • Recency windows (e.g. 7 → 14 days) • Signal decay (older activity mattered less) • Confirmation rules (e.g. 2 stakeholders instead of 1) This avoided the feeling that “the rules keep changing.” 3. Protect rep time with guardrails Any experiment that increased volume had to meet a simple bar: Did meeting-set rate or speed-to-first-touch improve? If not, we rolled it back quickly. 4. Make changes visible and explainable We shared a short changelog with reps: “We tightened X because Y was creating false positives.” Transparency mattered more than perfection. 5. Manager reinforcement Frontline managers previewed changes before rollout and coached to them in 1:1s. That kept trust intact even when thresholds shifted. The key insight: reps don’t need perfect intent models—they need predictable ones. As long as changes feel incremental, explainable, and tied to outcomes, adoption stays high. Hope you understand that now?