Instantly download the exact prompt library used in Axiom Academy — structured, deterministic, and built on the Systems Reliability Framework.
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See how the full course goes deeper →These 25 don't. Here's the structural difference.
"Analyze this lead and tell me if they're a good fit for our product."
→ Output format: unpredictable
→ Null field handling: none
→ Confidence: not surfaced
→ Audit trail: zero
→ Production-safe: ✗
"Classify ICP fit as [HIGH / MEDIUM / LOW / UNKNOWN].
Rules: HIGH requires company_size ≥ 50 AND industry IN [saas, fintech] AND title CONTAINS [ops, revenue]. If any required field is null → return UNKNOWN.
Output JSON only: {fit_score, confidence_pct, reason, missing_fields[]}"
→ Structured output: JSON schema
→ Null field handling: explicit
→ Confidence: always returned
→ Audit trail: built-in
→ Production-safe: ✓
Deterministic ICP fit scoring, BANT classification, job-title normalization, and firmographic scoring — all returning structured JSON with confidence thresholds.
Company enrichment verification, data quality scoring, field normalization, contact-to-account matching — with explicit UNKNOWN fallbacks and missing-field reporting.
Early-warning churn signals, stalled deal detection, pipeline coverage analysis, and engagement-drop classification with severity tiers and recommended next actions.
Graceful degradation patterns, unknown-field handling, retry-with-context prompts, structured error reporting — the prompts that make workflows safe to run unattended.
The 7-principle Systems Reliability Framework condensed to a single PDF sheet — the conceptual foundation behind every prompt in this pack and every module inside Axiom Academy.
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Knowing what prompts to use is step one. Axiom Academy teaches you how to build systems around them — with data contracts, observability, graceful degradation, and the full Systems Reliability Framework — so your automations work reliably in production, not just in demos.
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