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Modernizing Software Documentation with AI and CLMs
In the ever-evolving tech world, keeping software documentation up-to-date is key to smooth onboarding and resolving issues effectively. Enter Compact Language Models (CLMs) and generative AI — game changers in the documentation landscape!
Transform your documentation workflow with AI-powered solutions that keep your docs current, comprehensive, and user-friendly. These cutting-edge technologies are revolutionizing the way teams create and maintain their documentation.

🔍 Human Review & Refinement
While CLMs are great at generating content, human oversight ensures accuracy, proper tone, and adherence to standards.
⚙️ Integration & Automation
Automating content updates in the CI/CD pipeline allows AI-generated content to seamlessly fit into existing workflows.
📚 Use Case Specifics
The success of CLMs in documentation hinges on the use case and the quality of training data.
Setting Up Your Help.md File in Git
Create Your Help.md File
Start by creating a Help.md file in your Git repository. This file serves as the primary documentation for your software. Use Markdown syntax to structure your content, including headings, lists, and links, for improved readability and navigation.
Implement Version Control
Commit your Help.md file to your Git repository. This ensures that all changes are tracked, enabling collaboration and version history. Use branches to manage different versions of your documentation, facilitating seamless updates and revisions.
Automate Documentation Updates
Integrate documentation updates into your CI/CD pipeline to ensure your Help.md file stays current as your software evolves.
Leveraging Compact Language Models (CLMs)
Preprocessing Your Help.md File
Use tools like Python's markdown module or frontmatter to parse your Markdown files into a structured format that CLMs can process. Retain the semantic structure (e.g., headings and lists) to ensure accurate content interpretation.
Prompt Engineering with GPT-3.5 Turbo
Instead of traditional fine-tuning, leverage prompt engineering to interact with GPT-3.5 Turbo. Create clear and specific prompts using examples from your Help.md file to generate accurate and relevant content.
Automating with LangChain
LangChain provides an easy way to automate content updates and integrate various language models seamlessly. Use it to fetch content from your Help.md file, process it with GPT-3.5 Turbo, and output structured FAQs.
Implementing AI-Driven Documentation
Prepare Your Data
Extract content from your Help.md file and preprocess it using a Markdown parser to maintain structure and context.
Generate Content Using AI
Use GPT-3.5 Turbo with carefully crafted prompts to transform your documentation into FAQs, guides, and summaries.
Integrate the Output
Incorporate AI-generated content back into your Help.md file or separate documentation files.
Review and Iterate
Validate the content and refine your prompts or workflows as needed to ensure accuracy and relevance.
Recommended Tools and Models
Compact Models
  • OpenAI GPT-3.5 Turbo: Efficient for lightweight applications
  • Hugging Face DistilBERT: Compact for summarization tasks
  • Flan-T5 (Google): Optimized for Q&A and summarization
Libraries and Frameworks
  • Hugging Face Transformers: Supports various CLMs with pre-built pipelines
  • LangChain: Builds context-aware applications with LLMs
  • Markdown-it: JavaScript library for robust Markdown parsing
Integration Tools
  • UnstructuredMarkdownLoader: Extracts content from Markdown
  • PromptTemplate: Creates consistent AI prompts
  • Pipeline: Simplifies model implementation
Code Examples: Implementing AI Documentation
Using GPT-3.5 Turbo with Prompt Engineering
# Example prompt:
Using the content below, generate five FAQs with answers:
# Installation
To install the software, run `setup.exe` and follow the instructions.
Test and iterate on prompts to ensure the model generates accurate and relevant content for your documentation needs.
Automating Content Updates with LangChain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
# Define the template
template = """
Below is the content from `Help.md`. Generate five FAQs based on it:
{content}
"""
prompt = PromptTemplate(input_variables=["content"], template=template)
# Load GPT-3.5 Turbo through LangChain
llm = OpenAI(model="gpt-3.5-turbo")
# Example content
help_md_content = """
## Installation
To install the software, download `setup.exe` and follow the installation
wizard.
"""
# Generate FAQs
faqs = llm(prompt.format(content=help_md_content))
print(faqs)
Using Hugging Face Transformers for Summarization
from transformers import pipeline
# Load a summarization pipeline
summarizer = pipeline("summarization", model="t5-small")
# Example input from Help.md content
help_content = """
## Installation
To install the software, download `setup.exe` from the official site and
follow the installation wizard.
"""
# Generate a summary
summary = summarizer(help_content, max_length=50, min_length=25,
do_sample=False)
print(summary[0]['summary_text'])
Advantages of AI-Driven Documentation
Efficiency
CLMs streamline the process by generating and updating documentation rapidly, reducing manual effort and allowing developers to focus on coding.
Consistency
Ensure uniform tone and style across all documents, enhancing readability and professionalism for a better user experience.
Scalability
Adapt and expand documentation easily as your product evolves, keeping content relevant and comprehensive without proportional effort increase.
Dynamic FAQs
Use embeddings to create real-time, AI-powered FAQs that address user queries with precision and contextual awareness.
Getting Started with AI Documentation

Master Your Documentation
Create comprehensive, always-current software docs
Implement AI Workflows
Integrate CLMs into your documentation process
Structure Your Help.md
Create well-organized documentation in Git
Start your journey to better documentation today by setting up a Help.md file in your Git repository and exploring the AI tools mentioned in this guide. For more information, refer to the documentation for OpenAI, Hugging Face Transformers, and LangChain.