Claude 4.7 Introduces Habits That Degrade Legacy Prompts

Newer models like Claude Opus 4.7 outperform predecessors on most tasks but regress on others due to shifted instincts: stricter literal interpretation, adaptive response lengths via new 'adaptive thinking' mode, direct-less-personal tone, and skipping tools when it deems them unnecessary. Anthropic's model change docs confirm these shifts. Impact: Prompts relying on vague phrasing, implicit lengths, old tone cues, or optional tools fail—e.g., lead qualifiers misjudge 'worth pursuing,' outputs vary from 2-15 bullets, writing loses warmth, CRMs go unupdated. Fix by auditing top 3-5 daily/high-stakes Claude projects/skills, subtracting hand-holding since smarter models need precision over volume.

15-Min Canary Test: 4 Checks to Restore Reliability

Test 3-5 critical prompts with identical inputs on Opus 4.7 vs. prior outputs.

Clarity: Replace fuzzy terms like 'worth pursuing,' 'appropriate,' 'handle correctly,' 'flag important,' 'strategic.' Define explicitly—e.g., 'worth pursuing' means 'company >50 employees, contact director+, prior chats show pain points.' Vague prompts trigger AI clarification requests or wrong actions.

Length: Adaptive thinking causes inconsistent outputs (e.g., 2, 5, or 15 bullets). Enforce via prompt: 'Always return exactly 5 bullets, one sentence each.' Ensures uniformity regardless of task complexity.

Tone: Opus 4.7 is more direct/less personal; old cues like 'warm, casual, conversational' mismatch. Teach via 3-5 diverse examples (e.g., your emails/LinkedIn posts) in knowledge base: 'Match these samples' rhythm, openers, sentence lengths.' Shifts from telling to showing voice.

Actions/Tools: Smarter model skips tools (Gmail, CRM, task trackers) if it thinks they're optional—e.g., drafts email but skips Airtable CRM update from transcript. Mandate: 'For every transcript, MUST update Airtable CRM first, then draft Gmail, then add task—before final response.' Prevents silent failures discovered weeks later.

Golden Inputs/Outputs Prevent Future Regressions

For each of 3-5 key use cases, archive 'golden' input (e.g., transcript/request) and best-ever output from old model, labeled by model/date/use-case. On upgrades, re-run golden input through new model and compare. Reveals exact degradation (e.g., skipped tool, wrong length), enabling targeted prompt fixes. This baseline catches issues immediately, avoiding production surprises.

Smarter Models Demand Subtraction Over Addition

As intelligence rises, trim prompts: remove excess guidance since every word now counts more. Prioritize specificity in remaining instructions—e.g., explicit definitions, mandatory steps—yielding better results than verbose hand-holding.