Prysai/Prysai-LLM-Playbook
A six-language playbook teaches people to verify LLM answers instead of trusting fluent-sounding output
Prysai-LLM-Playbook is a book-style repository teaching how to work with language model tools like ChatGPT, Codex, Claude Code, Gemini, DeepSeek, and Grok. Instead of jumping straight into a specific platform, it starts with a five-unit foundation that explains why a fluent LLM answer can still be wrong and how to check it before trusting it. The project openly labels itself 'candidate' status, meaning learner completion and real-world effectiveness have not yet been verified.
What it does
- Teaches a repeatable loop across five units: define the task, choose a bounded action, inspect the result, keep evidence, and state the limit
- Forces readers to understand what an LLM can and cannot do before entering any specific platform track like Codex
- Includes a 5-minute, no-setup prompt exercise (no Git or terminal needed) that lets anyone see an LLM add unrequested details to a rewritten message
- Publicly reports its own current state: 22 English chapters, 18 labs (all in draft, not run), 25 Skills, and 40 evaluation fixtures (structure-checked only, not scored)
- Separates findings into an 'evidence ledger' distinguishing what was actually observed, what was merely captured but unscored, and what remains completely unknown
Why it matters
For anyone adopting LLM tools at work, this offers a way to build a habit of verifying outputs rather than trusting confident-sounding answers by default. Its transparency about unverified claims also makes it a more trustworthy reference amid widespread overstated AI productivity claims.
Terms in this repo
- LLM · Large language model, an AI trained on text to generate answers
- candidate status · The project's label meaning the structure exists but learner testing and independent review are not yet complete
- Skill · A bounded unit of action with defined triggers, exclusions, and failure handling
- fixture · A prepared, fixed example used for practice or testing
- evidence ledger · A table distinguishing what was actually observed, what was collected but unscored, and what remains unknown
Repository description (English)
An evidence-led, six-language LLM playbook: the transferable core, the Codex flagship track, and adapters for ChatGPT, Claude Code, Gemini, DeepSeek, and Grok.
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