Technical Writing

AI-Powered Gap Analysis: Uncovering Missing Pieces in Your Technical Documentation

Posted on by Fionn
24
Mar

Welcome back to the I Heart Technical Writing series! In this installment, we’re moving beyond high-level concepts and diving into practical implementation. We’ll explore how you can use AI to identify gaps in your existing documentation, ensuring that you’re providing comprehensive and effective support for your users.

If you’re just joining us, catch up on the previous articles: AI-Powered Topic Generation and Outlining: Breaking the Writer’s Block Barrier, AI-Powered Content Review: Accelerating the Editing Process, and AI for Personalization and Localization: Delivering Tailored Documentation Experiences.

In a previous article, we suggested that AI could “Suggest five new troubleshooting topics for our software’s error messages related to database connectivity. Include common causes, diagnostic steps, and potential solutions.” But how do we actually do that? That’s what we will demonstrate in this tutorial.

Before you Start

This tutorial is designed for both technical writers and programmers who are interested in leveraging AI to improve their documentation. Whether you’re a seasoned writer looking to automate your workflow or a developer eager to contribute to your project’s documentation, you’ll find valuable insights here.

To get the most out of this tutorial, you should have the following:

  • Basic Understanding of Large Language Models (LLMs): Familiarity with the concept of LLMs and how they can be used to generate text.
  • Moderate Python Coding Skills: We’ll be using Python to interact with the OpenAI API, so some coding experience is required.
  • OpenAI API Key: You’ll need an API key to access the OpenAI API. You can obtain one by creating an account on the OpenAI website.
  • Familiarity with Your Documentation Set: A solid understanding of the existing documentation is essential to identify potential gaps.

The Problem: Knowledge Gaps and User Frustration

Imagine a user encounters an error while using your software and turns to your documentation for help. If the documentation doesn’t cover that specific error, the user is left frustrated and without a solution. These knowledge gaps can lead to decreased user satisfaction, increased support requests, and ultimately, a negative impact on your product’s adoption. Manually identifying these gaps is challenging and time-consuming, requiring you to analyze user feedback, track support tickets, and conduct extensive research.

The AI Solution: Let AI Fill the Void

AI can help automate the process of identifying documentation gaps by analyzing user search queries, support tickets, and other data sources to identify topics that are not adequately covered. By leveraging AI, you can proactively address these gaps and ensure that your documentation provides comprehensive support for your users.

Practical Steps: Uncovering Documentation Gaps with AI

Here’s a step-by-step guide to using AI to identify documentation gaps:

1. Gather Your Data:

The first step is to collect the data sources that can provide insights into potential documentation gaps. This may include:

  • User Search Queries: Analyze the search queries that users are entering on your documentation website. If users are frequently searching for a specific topic that is not well-documented, it’s a sign of a potential gap.
  • Support Tickets: Review support tickets to identify common issues that users are encountering. These issues may represent areas where your documentation is lacking.
  • User Feedback: Collect user feedback through surveys, forums, and other channels. Pay attention to comments and suggestions that highlight missing or unclear information.

2. Prepare Your Data:

Once you have collected your data, you’ll need to prepare it for analysis. This involves cleaning the data, removing irrelevant information, and formatting it into a consistent format.

  • Clean the Data: Remove any irrelevant or duplicate entries from your data.
  • Tokenize the Data: Split the text into individual words or tokens using libraries like NLTK or spaCy.
  • Format the Data: Convert your data into a format that is suitable for analysis, such as a CSV file or a Pandas DataFrame.

3. Interact with the OpenAI API:

Use the OpenAI API to analyze your data and identify potential documentation gaps. Here’s how to do it:


import openai
import os

# Load your API key from an environment variable
openai.api_key = os.environ.get("OPENAI_API_KEY")

def analyze_queries(queries):
prompt = f"""
You are a technical documentation expert.  Based on the following list of user search queries,
identify 5 topics that are not adequately covered in our documentation. For each topic, 
suggest a title for a new documentation article.  Format the response as a list of titles.

Queries:
{queries}

Titles:
"""

response = openai.Completion.create(
engine="text-davinci-003", # Or a suitable model
prompt=prompt,
max_tokens=150,
n=1,
stop=None,
temperature=0.7,
)

return response.choices[0].text.strip().split("n")

# Example usage
user_queries = [
"database connection failed",
"troubleshooting database errors",
"AWS kubernetes config errors",
"how to connect to postgres",
"what is the max AWS upload size",
"slow database queries",
"troubleshooting slow database queries",
"resolve AWS config file errors",
"diagnose DB connectivity issues",
"Kubernetes deployment errors"
]

suggested_topics = analyze_queries(user_queries)
print("Suggested Documentation Topics:")
for topic in suggested_topics:
print(f"- {topic}")

4. Evaluate the Results:

Review the list of suggested topics generated by the AI and determine which ones represent genuine documentation gaps.

  • Cross-Reference with Existing Documentation: Check if the suggested topics are already covered in your documentation.
  • Assess User Relevance: Determine if the suggested topics are relevant to your users and address their actual needs.
  • Prioritize Based on Impact: Prioritize the topics that are most likely to have a significant impact on user satisfaction and product adoption.

5. Create New Documentation:

Once you have identified the documentation gaps, it’s time to create new documentation to fill those gaps.

  • Write Clear and Concise Content: Write easy-to-understand content that addresses the specific needs of your users.
  • Provide Examples and Use Cases: Include examples and use cases to help users understand how to apply the information in practice.
  • Gather Feedback and Iterate: Collect feedback on your new documentation and iterate to improve its accuracy, clarity, and effectiveness.

Benefits: Proactive Documentation for Happy Users

By implementing AI-powered gap analysis, you can achieve the following benefits:

  • Improved User Satisfaction: Provide comprehensive documentation that addresses the needs of your users, leading to higher satisfaction and engagement.
  • Reduced Support Requests: Proactively address potential issues by providing documentation that covers common problems and troubleshooting steps.
  • Enhanced Product Adoption: Make it easier for users to learn and use your products, leading to increased adoption and success.

Bringing it All Together: Closing the Knowledge Gap

In this article, we’ve explored how you can use AI to identify gaps in your existing documentation, ensuring that you’re providing comprehensive and effective support for your users. By leveraging ‘AI-powered gap analysis’, you can proactively address potential issues, improve user satisfaction, and enhance product adoption.

About the Authors

This article was brought to you by I Heart Technical Writing, a team of passionate technical writers dedicated to providing tools, templates, and tutorials for the technical documentation community. Visit our website at https://www.ihearttechnicalwriting.com/ to learn more.

Fionn