Release notes play a critical role in how product teams communicate new features, bug fixes, and improvements to both internal stakeholders and end users. Yet, generating these notes is often tedious, manual, and inconsistently written—especially when teams scale. What if you could automate this process using AI? That’s where Dify AI steps in. With its flexible, no-code/low-code interface and powerful LLM integration capabilities, Dify AI makes it easy to build a release-note generator that transforms raw commit messages, pull requests, and change logs into polished, structured summaries. In this blog, we’ll walk you through how to use Dify AI to build an internal tool for release note generatỏ that fits seamlessly into your product development workflow.

Why Automate Release Notes?

Release notes are a crucial, yet frequently overlooked, part of the software development lifecycle. While everyone agrees they’re important for keeping users informed and engaged, the process of creating them often becomes a bottleneck.

The Problem with Manual Release Notes

Developers are swamped with coding, product managers might not have the granular technical details, and technical writers spend valuable hours deciphering obscure commit messages to craft understandable updates. This is where automating your release notes becomes a game-changer.

Benefits of Automating Release Notes

Standardized Communication

Automating this process helps standardize communication. You can define templates and rules that ensure a consistent tone, format, and level of detail in every release. This eliminates guesswork and ensures your users always know what to expect.

Reduced Overhead

Automation significantly reduces manual effort. An automated system can pull information directly from version control systems, project management tools, or other relevant sources. This frees developers to focus on coding and allows product managers and writers to work on higher-value tasks.

Reliable Documentation for Every Release

Most importantly, automation ensures every release gets properly documented. No more skipped updates due to time constraints. An automated system can generate release notes with each deployment, keeping users informed and reinforcing transparency.

Why Choose Dify AI for the Job?

So, you’re convinced about the power of automated release notes. But with so many tools and approaches out there, which one should you choose to bring this vision to life? This is where Dify AI shines as an exceptionally strong candidate for the job.

Dify AI stands out primarily because it offers an open-source framework specifically designed for building tailored AI assistants and automation flows using large language models (LLMs). This is a crucial distinction. Unlike proprietary, black-box solutions, Dify AI gives you the flexibility and transparency to truly customize your release note generation process. You’re not just using a tool; you’re building your tool on a robust foundation.

One of Dify AI’s key strengths is its native support for prompt design. This means you can directly control how the LLM interprets your source data (like commit messages or task descriptions) and what kind of output it should produce. Do you need a formal tone for enterprise clients? A more casual, engaging tone for consumer apps? Dify AI allows product teams to precisely customize the logic and tone of release notes generation without needing deep AI expertise or extensive custom coding.

Furthermore, Dify AI provides robust capabilities for document indexing and real-time interaction. This is incredibly powerful for release note automation. You can feed Dify AI your project documentation, feature specifications, or even past release notes, allowing the LLM to understand the context and generate more accurate and relevant summaries. The real-time interaction capabilities mean you can iterate on prompts and refine the output quickly, ensuring the generated notes meet your exact requirements.

The most compelling reason to choose Dify AI is that it empowers you to achieve a highly customized solution without needing to reinvent the wheel or write a full backend from scratch. Dify AI streamlines LLM integration, data handling, and UI setup—so your team can focus on crafting release note logic instead of backend AI engineering.

Overview of the Release-Note Generator Workflow

Building a release-note generator with Dify AI leverages the power of large language models to transform raw development data into polished, user-friendly updates. The workflow is designed to be efficient and highly customizable, ensuring your release notes are always accurate, relevant, and consistent.

Here’s a detailed look at how this automated release-note generator works:

1. Ingesting Raw Development Data

The first step in generating release notes is feeding the system the raw information about what has changed. Our Dify AI-powered tool is designed to receive input from various crucial sources within your development ecosystem:

• Git Commit Logs: These are the granular records of every change made to your codebase. While often terse and technical, they contain the fundamental details of what was done. The tool can parse these messages, extracting key information.
• Merged Pull Requests (PRs): Pull request titles and descriptions often provide a higher-level summary of a change, including the intent behind it. These are invaluable for understanding the user-facing impact of a technical change.
• JIRA Ticket Summaries/Descriptions: For teams using JIRA (or similar project management tools like Asana, Trello, etc.), the summaries and descriptions of completed tasks, stories, or bugs offer excellent human-readable context. These often describe the problem being solved or the feature being delivered from a product perspective.

By pulling from these diverse sources, the Dify AI application gains a comprehensive understanding of the development work completed, moving beyond just code changes to capture the broader product context.

2. Intelligent Processing with Dify AI’s Core Capabilities

Once the raw data is ingested, it’s sent to the heart of the system: the custom prompt and logic defined within Dify AI. This is where the magic of the large language model comes into play, guided by your specific instructions.

• Custom Prompt Engineering: This is the most critical component. Within Dify AI, you’ll design a prompt that instructs the LLM on how to interpret the input data. For example, your prompt might tell the LLM to:
  • “Summarize the following commit messages into a concise, user-friendly update.”
  • “Identify if the change is a new feature, a bug fix, or an improvement.”
  • “Translate technical jargon into language understandable by end-users.”
  • “Maintain a formal/informal/engaging tone.”
  • “Extract key benefits for the user.”
• Logic and Orchestration: Beyond just a single prompt, Dify AI allows you to build more complex workflows. This might involve:
  • Filtering: Ignoring certain types of commits (e.g., build system updates) that aren’t relevant to end-users.
  • Categorization: Using the LLM to automatically categorize each change into predefined sections like “New Features,” “Bug Fixes,” “Improvements,” or “Performance Enhancements” based on keywords, sentiment, or learned patterns.
  • Contextualization: Leveraging Dify AI’s document indexing capabilities to provide the LLM with additional context, such as product definitions, past release note styles, or a glossary of terms, ensuring the output is highly relevant and consistent with your brand voice.
  • Iterative Refinement: Dify AI’s real-time interaction features allow you to test and refine your prompts and logic quickly, observing the output and making adjustments until it meets your quality standards.

3. Generating Formatted, Human-Readable Output

The final stage is the output of the processed information into clear, actionable release notes. The Dify AI workflow ensures the generated content is not just accurate but also well-structured and easy for your users to consume.

• Formatted and Clear: The LLM, guided by your prompt, will rephrase technical details into simple, understandable language. It will remove developer-centric jargon and focus on the “what” and “why” from a user’s perspective.
• Categorized by Type: One of the most valuable outputs is the automatic categorization. Instead of a flat list of changes, your release notes will be neatly organized under headings like:
  • New Features: Highlighting exciting new functionalities.
  • Bug Fixes: Acknowledging and reassuring users that issues have been resolved.
  • Improvements: Detailing enhancements to existing features or user experience.
  • Performance Enhancements: Informing users about speed or efficiency upgrades.
  • Security Updates: Communicating crucial security patches.
• Human-Readable: The ultimate goal is release notes that users actually want to read. By leveraging Dify AI, the output is designed to be engaging, informative, and tailored to your audience, making sure every release gets the attention it deserves.

This systematic workflow, powered by Dify AI’s flexible and powerful LLM capabilities, transforms the often-cumbersome task of creating release notes into an automated, intelligent, and highly effective process.

Step-by-Step Guide to Building the Generator

Step 1: Prepare Your Input Sources

Connect your VCS (like GitHub or GitLab) and extract relevant data for each release—commit messages, PR titles/descriptions, linked issue summaries. You may export this as a Markdown file, or use Dify’s API for real-time integration.

Step 2: Define Your Prompt Template in Dify AI

Craft a structured prompt. For example:
“You are a product documentation assistant. Your job is to generate a release note from the input below. Summarize changes in a friendly tone. Group them under: New Features, Improvements, and Bug Fixes. Remove technical jargon and avoid referencing commit hashes.”

Step 3: Upload Sample Data and Test Output

Feed the AI with real example data from your past releases. Test how well the prompt transforms technical updates into user-friendly summaries. Iterate as needed to improve tone, structure, and accuracy.

Step 4: Customize Output Format

Configure the assistant to output Markdown, HTML, or plain text depending on how your team publishes release notes (e.g., in your changelog site, emails, or Notion docs). Add bullet formatting, headers, and release version info.

Step 5: Deploy in a Team-Friendly Interface

Deploy your assistant to a web UI, Slack bot, or internal portal where product managers and developers can easily upload release data and get instant output. Optionally integrate with GitHub Actions or Jira workflows for auto-triggered generation.

Best Practices for Effective Results

To get the most out of your release-note generator built with Dify AI, it’s crucial to align both your input data and prompt strategy with real-world usage. AI is only as good as the data and guidance it receives. Below are key practices to improve the clarity, accuracy, and usefulness of your release notes.

1. Use Clean and Consistent Commit Messages

The quality of your release notes depends on the quality of your inputs—especially commit messages. Avoid vague entries like “fix bug” or “misc updates.” Instead, adopt standards such as Conventional Commits, which use clear tags like:

• feat: for new features

• fix: for bug fixes

• refactor: for code changes

• docs: for documentation updates

These structured messages help the AI better interpret the purpose and significance of each change.

2. Include Contextual Metadata

AI models perform best when given structured context. Add metadata to your inputs, such as:

• JIRA ticket types (Bug, Task, Story)

• Priority levels

• GitHub labels (e.g., frontend, backend, hotfix)

This allows the prompt to categorize items properly under headings like New Features, Bug Fixes, or Improvements. For example, a ticket labeled enhancement can be guided to appear under “Improvements.”

3. Enable Human Review Before Publishing

Even though Dify AI generates polished outputs, human review is critical—especially for external-facing release notes. Reviewers can:

• Catch sensitive info

• Adjust technical jargon for users

• Ensure brand tone and voice

Integrate a review step into your CI/CD workflow to allow product managers or technical writers to make quick edits or approve the final version before release.

4. Continuously Refine Prompts Using Team Feedback

Release notes serve many audiences—customers, sales, support, and marketing. Regularly collect feedback from these stakeholders to improve prompt performance. For example:

• Adjust tone (e.g., more friendly or formal)

• Reorganize layout

• Highlight key features based on user interest

Dify AI supports version-controlled prompt templates, making it easy to evolve your release-note assistant with each sprint.

Optional Enhancements

Once your Dify AI-powered release-note generator is up and running, there are several ways to enhance it beyond the basics. These optional upgrades can help your release notes better serve diverse user groups, align with brand voice, and even provide data-driven insights for product strategy. Below are four powerful enhancements to consider:

Multilingual support for global releases

If your product serves international markets, offering release notes in multiple languages enhances accessibility and customer experience. With Dify AI’s support for multilingual models, you can easily configure your assistant to output release notes in languages like Japanese, French, Vietnamese, or Spanish. Simply extend your prompt to include language-specific instructions (e.g., “Translate this into Spanish after generating the summary”), or create separate prompt templates per language. This ensures your updates are inclusive and professionally localized—without the need for external translation workflows.

Include internal vs external changelog versions

Not every update should be communicated the same way to internal teams and external users. Engineers may need full commit logs or technical context, while end users only care about what’s new or fixed from a usability standpoint. You can configure Dify AI to generate two versions of the same release note:
– Internal changelog: Includes technical jargon, references to ticket numbers, or deployment details.
– External changelog: Simplified, polished, and focused on business value and customer-facing changes.
> This separation reduces cognitive load for each audience and ensures you’re communicating the right level of detail to the right people.

Sentiment tuning (formal, playful, technical) for different audiences

Different products—and different brands—call for different tones. A SaaS product for accountants may need a formal, no-nonsense tone, while a startup targeting developers might prefer a more playful or casual voice. By adjusting the instructions in your Dify AI prompt, you can fine-tune the sentiment and personality of the release notes. Examples:
– Formal: “Improved performance of invoice generation module.”
– Playful: “We taught invoices how to sprint—they’re much faster now.”
– Technical: “Optimized SQL query execution in invoiceService.ts using index hints.”
This flexibility makes your release communications feel native to your brand and culture.

Combine with analytics to surface feature adoption data in notes

Imagine a release note that says, “New dashboard added—used by 3,000+ users in the first week.” With tools like Mixpanel or Amplitude, you can enrich AI prompts with real usage data for smarter summaries. This helps stakeholders understand the impact of each release—not just the content. You can even categorize updates by adoption tiers or user feedback sentiment for deeper insight.

Conclusion

Building a release-note generator using Dify AI gives your product team a powerful tool to automate and standardize communication. It turns scattered developer updates into clean, structured summaries—saving hours of manual work while ensuring no change goes undocumented. With Dify AI, you save time while improving how your product communicates. Whether releasing weekly or quarterly, it helps turn updates into clear, consistent messages.