# AI use cases for Prysai LLM Playbook (Prysai/Prysai-LLM-Playbook)

Repo: [Prysai/Prysai-LLM-Playbook](https://github.com/Prysai/Prysai-LLM-Playbook) | Use cases: 4 | Page: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/

A bounded, English-first LLM curriculum that teaches how to make one limited request to a language model, inspect the answer, and carry the method to a new task before choosing a platform.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Check an LLM answer before you act on it: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/check-llm-answer-before-acting/
- Turn a vague request into an agent task card: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/write-bounded-agent-task-card/
- Check a citation-shaped answer before you use it: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/check-citation-shaped-answer/
- Review a 'done' claim against its evidence: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/review-done-claims-with-evidence/

## How to check an LLM answer before you act on it with Prysai LLM Playbook

Repo: [Prysai/Prysai-LLM-Playbook](https://github.com/Prysai/Prysai-LLM-Playbook) | Difficulty: Beginner | Time: 60 minutes | Basis: From the README

Use the five-unit core route to write a bounded request and inspect the model's answer.

**Who it is for:** A beginner who has received a fluent AI answer and needs to decide whether it is safe to use.

**The problem:** A fluent answer can omit facts, invent details, or sound certain without support, and it is hard to tell which.

**The outcome:** A bounded task card, a checked result record that states what remains unknown, and one transfer attempt on a new task.

### Steps

1. Open the guided reading site and start the five-unit LLM foundation route.
2. Read the LLM concepts guide to see what a language model can and cannot establish.
3. Write a bounded task card with the goal, supplied context, allowed help, limits, check, and stop condition.
4. Send one bounded request and mark any omission, invention, or overconfidence you find in the answer.
5. Write a checked result record that lists what you inspected, what you kept, and what remains unknown, then repeat the method on a new task.

### Prompt to paste into your agent

```text
Using the Prysai-LLM-Playbook repository, read book/routes/llm-foundation-core-v1-EN.md and write a bounded task card for <task>. The expected output is a card with goal, context, allowed help, limits, check, and stop condition. Verify that every limit is stated before any request is sent.
```

### Caveat

The route is a candidate, and learner completion, transfer, and retention are not yet measured, so finishing it does not prove you can use the method reliably.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/check-llm-answer-before-acting/

## How to turn a vague request into an agent task card with Prysai LLM Playbook

Repo: [Prysai/Prysai-LLM-Playbook](https://github.com/Prysai/Prysai-LLM-Playbook) | Difficulty: Intermediate | Time: 60 minutes | Basis: From the README

Use Chapter 3 and Lab 002 to define goal, constraints, acceptance, and stop conditions.

**Who it is for:** A person about to hand a loosely described job to an AI agent who needs it to be specific enough to execute.

**The problem:** A broad request leaves the agent to guess the scope, the success check, and when to stop.

**The outcome:** A task protocol with goal, context, constraints, acceptance criteria, stop conditions, and failure handling.

### Steps

1. Open Chapter 3 of the book and read its task protocol section.
2. Complete Lab 002 using one real request you want to hand off.
3. Write the goal, context, constraints, and acceptance check as separate lines.
4. Add a stop condition and a plan for failure handling.
5. Give the finished protocol to the agent and check that its output matches the acceptance check.

### Prompt to paste into your agent

```text
In the Prysai-LLM-Playbook repository, follow book/chapters/03-task-protocol-EN.md and Lab 002 to convert <request> into a task protocol. The expected output includes goal, context, constraints, acceptance, stop conditions, and failure handling. Verify that the acceptance check can be run without asking the agent for judgment.
```

### Caveat

The repository states its structure and static checks exist, but learner runs and independent review are still pending, so the protocol is a method to test rather than a proven one.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/write-bounded-agent-task-card/

## How to check a citation-shaped answer before you use it with Prysai LLM Playbook

Repo: [Prysai/Prysai-LLM-Playbook](https://github.com/Prysai/Prysai-LLM-Playbook) | Difficulty: Beginner | Time: 30 minutes | Basis: From the README

Use the source-record check to find missing source fields in an AI answer.

**Who it is for:** A researcher or student who has an AI answer full of references and needs to know which claims have a checkable source.

**The problem:** Citations that look real may lack the fields needed to find or verify the source.

**The outcome:** A list of visible missing source fields and a next check, or a stop decision.

### Steps

1. Open the source-record check in the communication clinic chapter.
2. Copy the answer you want to test into your notes.
3. Mark each citation that is missing a source field.
4. Decide whether to run the next check or stop using that claim.
5. Record the result as candidate, not as a verified source.

### Prompt to paste into your agent

```text
Using the Prysai-LLM-Playbook repository, apply the source-record check in book/communication-clinic-EN.md to the answer <answer>. The expected output is a list of citations with missing source fields and one next check or stop decision. Verify that no source is marked verified unless its fields were actually inspected.
```

### Caveat

The check uses a fixed fictional answer in the repository and its learner outcome is not run, so it only flags missing fields and does not confirm that any source is real.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/check-citation-shaped-answer/

## How to review a 'done' claim against its evidence with Prysai LLM Playbook

Repo: [Prysai/Prysai-LLM-Playbook](https://github.com/Prysai/Prysai-LLM-Playbook) | Difficulty: Intermediate | Time: 45 minutes | Basis: From the README

Use Chapter 9 and Lab 003 to match each claim to the evidence that supports it.

**Who it is for:** A developer or team lead who has been told a task is finished and needs to confirm it before accepting the work.

**The problem:** An agent or tool can report completion while touching wrong files, skipping tests, or missing part of the scope.

**The outcome:** A claim-to-evidence review that catches wrong files, missing tests, and scope gaps.

### Steps

1. Read Chapter 9 on verification and recovery in the book.
2. List every claim the tool made as a separate line.
3. Match each claim to the file, test, or output that supports it.
4. Mark claims with no matching evidence as unproven.
5. Decide to accept, revise, or stop, and record what remains unknown.

### Prompt to paste into your agent

```text
In the Prysai-LLM-Playbook repository, use book/chapters/09-verification-and-recovery-EN.md and Lab 003 to review the completion claims in <report> against the files in <target>. The expected output is a claim-to-evidence table with unproven items marked. Verify that each accepted claim points to a file, test, or output you actually inspected.
```

### Caveat

The lab is a draft exercise in the repository, so it helps structure a review but does not guarantee that a completed task is correct.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/prysai-prysai-llm-playbook/review-done-claims-with-evidence/