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With the growing popularity of vibe coding, businesses need standards and workflows to make it evolve sustainably.
Prompt journals are an essential part of this foundation. They document how the AI-generated code was createdwhich greatly facilitates audits, maintenance and knowledge transfer.
Vibe coding uses natural language prompts to generate code. Keeping a prompt journal allows you to capture the intention, decisions, and process behind the outcome.
These are ideas for creating a journal prompt, so adapt them as needed. Every organization has its own needs and culture. Start somewhere, even if it’s with a simple template. The table below shows the main fields to include in a prompt log.
| Category | Field name | Description and objective of the audit | Example of value |
| Identify | Log ID/Timestamp | Unique entry ID and Coordinated Universal Time (UTC) time for time tracking | PL-992 / 2024-05-20 14:00Z |
| Developer ID | The human responsible for the prompt and its outcome | dev_jsmith_01 |
|
| Ticket reference | Connects AI work to a business requirement | PROJ-104 |
|
| Technical | Model and initial version | The specific endpoint used (essential for reproducibility) to begin refining the prompt | gemini-1.5-pro-002 |
| Model and version | The specific endpoint used (essential for reproducibility) for the final execution | CDP_version23 |
|
| Seed | The deterministic DNA of generation | 4294967295 |
|
| Hyperparameters | Values such as Temperature, Top-P and Top-K | Temp: 0.7, Top-P: 0.9 |
|
| System Prompt ID | Version of the persona or guardrails applied to the model | sys_v4.2_standard_dev |
|
| Content | Input prompt | The exact plain text sent to the AI after Data Loss Prevention (DLP) cleanup | "Update API to include CDP identifier field..." |
| Refinement loop | Any corrective follow-up prompt used to correct the mood | "Too verbose, use arrow functions." |
|
| Output link | Link to the specific commit or pull request (PR) generated by this prompt | (github.com/repo/pull/12)(https://github.com/repo/pull/12) |
|
| Compliance | DLP Status | Confirmation that no personally identifiable information (PII) or protected health information (PHI) was included in the prompt | PASSED |
| Security Analysis | Status of automated vulnerability testing on AI code | Snyk: 0 Critical, 0 High |
|
| attribution of intellectual property | Records whether the AI cited specific sources or licensed materials | MIT License (suggested) |
|
| Validation | Human examiner | The peer or manager who manually verified the AI result | lead_dev_ananya |
| Test coverage | Percentage of unit tests passed by generated code | 94% Coverage |
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What to include in each section of the journal
Identity section
The identity section distinguishes individual prompts. It records their iterations, the person who provided the prompt, and the tasks for each prompt.
- Log ID and timestamp: Designates an ID for each prompt and prompt iteration, and captures the time you run them.
- Developer ID: Identifies and assigns responsibility to the person who performed the prompt.
- Ticket reference: Associates the prompt with a specific task (for example, a JIRA or Workfront ticket number), revealing business requirements.
Technical section
The technical section provides information about the AI platform and the settings and conditions for each prompt.
- Model and initial version: Recording the AI platform and model associated with each prompt is critical to reproducing results. This field also helps refine prompts, as each platform and AI model works differently. Use this field for scenarios where you are refining prompts in a different system than the one you are running them on. This practice remains effective. For example, it may be less expensive to refine a prompt in a large language model (LLM) such as Claude or Gemini before using it in a martech tool, such as a customer data platform (CDP).
- Model and version: This field records the model and version of the AI system on which you ultimately run the prompt. This information is especially useful if you first refine the prompt in another system.
- Seed: When responding to prompts and generating results, AI platforms typically involve some randomness. For example, two people using the exact same prompt on the same platform and template will get related but unique results. AI platforms track these iterations via seed values. If you want to produce the same result from a prompt, the seed clarifies the variables in the generation process.
- Hyperparameters: These include prompt items such as temperature, Top-P, and Top-K. They regulate the degree of fine-tuning that the AI model allows when generating output. Like the seed, the coding of hyperparameters is essential for replication.
- System prompt ID: The system prompt ID is a value that AI Platform assigns to the prompt.
- Input prompt: This is the exact text of the prompt. This is one of the most critical parts of the journal.
- Refinement loop: The refinement loop follows the follow-up prompts. They help you refine the result to better meet the requirements.
- Output link: This is where you store the final result, such as a GitHub link. For image or text output, this could be a link to a digital asset management (DAM) platform, wiki, or office suite.
Compliance
The compliance section is essential for regulatory, legal and information security stakeholders. They will need to review this information to verify how the generative AI results align with the organization’s policies.
- DLP Status: Ensures adequate security and transmission to comply with various standards.
- Security Analysis: Preserves security analysis results, ensuring that code evaluation occurs before deployment to production.
- Attribution of intellectual property: Captures all sources cited by the model when generating code.
Validation
Although vibe coding speeds up software development, it does not reduce human responsibility. This section tracks who has reviewed and validated that the code meets the requirements and standards.
- Human reviewer: Identifies who reviewed and approved the code before deployment to production environments.
- Test coverage: Records the number of quality assurance (QA) test cases and user acceptance tests (UAT) that the code passed and failed, including those that were not considered critical.
Why You Should Keep a Prompt Journal
In addition to increasing productivity by refining prompts over time, prompt logs serve several other purposes.
Adhere to software standards
Software is already subject to many standards and auditing frameworks. As mood coding grows in popularity, these standards and audits may require prompt logs. External auditing organizations may request access to view prompt logs as part of their assessment processes.
Provide documentation to end users
When an organization hires a vendor or contractor to code new software, a prompt log is a useful deliverable. In addition to supporting ongoing maintenance of the software, the prompt log proves that the vendor or contractor has met expectations. This is typical when determining project progress and payment milestones.
Train new employees
Prompt logs can make training easier. During onboarding for mood coding roles, new team members can refer to the prompt logs. They won’t need to start from scratch when learning how to structure prompts.
Improve the effectiveness of prompts
These logs help organizations respond more effectively, saving time and money. This will become increasingly important as AI consumption costs rise.
Various AI platforms may charge different amounts for similar tasks. For example, refining a prompt in ChatGPT, Claude, or Gemini may cost less than doing it directly on a martech platform. Prompt logs can help determine the most cost-effective platform for each phase of work.
Determining the Right Model to Use
LLMs are constantly evolving. As new versions are deployed, their output for a given prompt changes. A prompt log tracks LLM results over time, which can inform how your organization should prompt.
Prompt logs are a useful artifact
Although prompt logs may seem like administrative work, they help mitigate risk and evolve the actions of people and systems. They provide value by tracking project progress and ensuring deliverables meet requirements.
Disclosure: I worked with Gemini to develop quick journal suggestions. Gemini produced the chart, and I explore and explain its contents using my own thoughts and experiences.
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