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Jev

Made Crystal Clear

A practical, visual guide to building software that makes fast, typed, calibrated decisions with TypeSafe's System One model

Jev is a System One model: instead of writing text, it answers typed questions about a state you send, with calibrated probabilities and a confidence value your code can act on. This guide takes you from a first Playground call to production: the three primitives (Choice, Score, Noul), how to build a good state, how to read confidence, the Python and JavaScript SDKs, the four core patterns (fan-out, confidence-gated routing, composite scoring, intent routing), pricing, and when an LLM is still the better tool. One rule throughout: keep code in control, and give the model narrow decisions.

Pages
280
Level
Beginner to advanced
Status
Available
Kindle · $12.99 ↗Paperback · $39.99 ↗Give feedback

Inside the book

  1. 01Why Jev: Decide, Don't Generate
  2. 02How Jev Works: State In, Typed Answers Out
  3. 03Your First Jev Call
  4. 04The Three Primitives: Choice, Score, Noul
  5. 05Designing States and Questions
  6. 06Confidence and Calibration
  7. 07The Four Patterns
  8. 08Recipes I: Guard and Verify
  9. 09Recipes II: Find, Extract, Classify
  10. 10The SDKs in Production
  11. 11Limits and Jagged Edges
  12. 12The Business Case
  13. 13Shipping to Production
  14. 14Learning Path, Cheat Sheet, and Resources

You'll leave with

  • See how a System One model differs from an LLM
  • Send a state and typed questions, and read the answers
  • Pick between Choice, Score, and Noul for each decision

For teams

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One licence, every book in the series, one link for everyone. From $250 a year.

See the team licences

Sources

Every source cited in Jev Made Crystal Clear. Each number matches the [n] marker next to that link in the book's "Go deeper" lists, so you can trace any claim back to its origin.

  1. [1]TypeSafe docs: Introduction
  2. [2]System One models
  3. [3]How to build with System One
  4. [4]AI primer
  5. [5]Models
  6. [6]Introducing System One Models & Jev
  7. [7]TypeSafe AI home page and FAQ
  8. [8]TypeSafe team
  9. [9]SiliconANGLE: TypeSafe AI exits stealth with $40M
  10. [10]DataCamp: System One models and Jev
  11. [11]InfoWorld on TypeSafe AI
  12. [12]Flavio Copes: A deep dive into Jev
  13. [13]OpenRouter: typesafe/jev-1.13
  14. [14]Vercel AI Gateway: Jev now available
  15. [15]Kahneman, Thinking, Fast and Slow (excerpt)
  16. [16]State
  17. [17]Primitives overview
  18. [18]HTTP API reference
  19. [19]Quick start
  20. [20]Parallel questions cookbook
  21. [21]GPT-4 Technical Report
  22. [22]The Rundown on Jev
  23. [23]Python SDK
  24. [24]Python SDK exceptions
  25. [25]JavaScript and TypeScript SDK
  26. [26]Agent skill
  27. [27]typesafe-ai/skills on GitHub
  28. [28]TypeSafe status page
  29. [29]Choice
  30. [30]Score
  31. [31]Noul
  32. [32]Advanced: structure
  33. [33]Python SDK: response types
  34. [34]Model jaggedness, jev-1.13
  35. [35]Classification using confidence
  36. [36]Liu et al., "G-Eval"
  37. [37]Zheng et al., "Judging LLM-as-a-Judge"
  38. [38]Hierarchical classification
  39. [39]Skill suggestion
  40. [40]Structure recovery (autoformat)
  41. [41]Confidence
  42. [42]Confidence-gated routing
  43. [43]Self-consistency, Noul
  44. [44]Self-consistency, Choice
  45. [45]SamuelSacco/jev-exploration
  46. [46]scikit-learn: Probability calibration
  47. [47]Guo et al., On Calibration of Modern Neural Networks
  48. [48]Xiong et al., Can LLMs Express Their Uncertainty?
  49. [49]Geifman and El-Yaniv, Selective Classification for Deep Neural Networks
  50. [50]Brier score
  51. [51]Patterns overview
  52. [52]Speculative fan-out
  53. [53]Composite scoring
  54. [54]Intent routing
  55. [55]Entity alignment cookbook
  56. [56]Smart home assistant demo
  57. [57]Example use cases
  58. [58]Guardrails for LLMs
  59. [59]Classifying RAG passages
  60. [60]Double-checking citations
  61. [61]Structured-data-extraction cascade
  62. [62]In-the-wild jailbreak prompts
  63. [63]Re-ranking cookbook
  64. [64]Line-by-line semantic find
  65. [65]Pre-parsed value extraction
  66. [66]Date extraction
  67. [67]Function calling
  68. [68]AutoResearch feature discovery
  69. [69]CLERC legal retrieval dataset
  70. [70]Shopify product taxonomy 2026-02
  71. [71]Python SDK usage guide
  72. [72]Python SDK retries reference
  73. [73]Python SDK constants
  74. [74]Python SDK changelog
  75. [75]JavaScript SDK API reference
  76. [76]JavaScript SDK changelog
  77. [77]Vercel AI Gateway zero data retention
  78. [78]facebook/bart-large-mnli
  79. [79]SetFit (Hugging Face)
  80. [80]TypeSafe workflow evals
  81. [81]Data Processing Agreement
  82. [82]system-one-adapter-python
  83. [83]OpenAI API pricing
  84. [84]Anthropic Claude pricing
  85. [85]Gemini API pricing
  86. [86]Python SDK API reference

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