From Manual to Machine: Enhancing Subject Matter Experts’ Productivity with AI

Introduction

Subject matter experts (SMEs) play an important role in designing assessments, ensuring accuracy, relevance, and quality of exam content. However, a significant part of the time of these experts is consumed by manual and less important tasks like reading lengthy content, identifying key information, and manually drafting exam questions.

As the demand for scalable and standardized assessments grows, it’s becoming increasingly important to streamline this process. This is where artificial intelligence (AI), especially generative AI, can play a transformative role—not by replacing SMEs, but by supporting them and enhancing their productivity.

In a recent webinar hosted by Ksolves, VP of Engineering Nishantal Agarwal shared insights into how generative AI is reshaping this process and freeing SMEs to focus on what they do best.

The Challenge: Manual Processes Holding Experts Back

Subject matter experts often feel a heavy burden with the traditional process of creating exam content. As described in the webinar, one of Ksolves’ clients—a certification authority in North America – was facing significant bottlenecks in exam creation.

“The speed at which they could include new content was limited by the subject matter expert’s ability to read, analyze, and manually enter that information,” said Nishantal Agarwal in the webinar. 

The implications of this were not just limited to delays, but also included: 

• Inconsistent quality of questions across exams
• Difficulty maintaining tone and grammatical precision is expected from professional certifications
• Limited scalability, as content development depended on manual input

The conclusion was clear: SME time was not being used efficiently, and manual tasks were diluting their skills.

The Opportunity: AI as a Workflow Enabler

To change the manual process of exam content creation into an efficient, AI-assisted pipeline, Ksolves integrated a robust technology stack. Each component was selected not only for its technical capability but for how well it supported the unique needs of exam generation – accuracy, scalability, contextual relevance, and linguistic precision.

Here’s a deeper look into how each technology contributed to the solution:

1. LangChain: The Orchestration Framework

LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. This served as the backbone of automating the entire pipeline for the certification authority. 

Its primary role is to orchestrate a seamless flow of data and operations, from content ingestion, semantic chunking, and contextual retrieval to question generation and final output validation. By using LangChain, the system could:

• Dynamically select relevant tools and models at each stage of the workflow
• Integrate external APIs, prompt templates, and retrieval mechanisms with minimal friction
• Maintain control over how inputs were processed and how outputs were formatted
• Enforce exam-specific rules (e.g., question types, tone, complexity) at every step

“LangChain helped us automate the whole workflow, from content extraction to validation, ensuring each question was structurally and grammatically aligned with exam standards,” shared Nishant Agarwal in the webinar.

This orchestration meant SMEs no longer had to manually oversee each part of the process, freeing them up for higher-order quality control and exam design.

2. Cassandra: Context-Aware Content Storage and Retrieval

The role of Cassandra in this solution extended far beyond traditional database functions. It was used as a vector database, a store for semantically rich representations of the source content.

Here’s how it supported the system:

• Semantic indexing: Using embeddings, content was broken down into smaller, meaningful chunks and stored as vectors for relevance-based retrieval
• Hybrid query support: Cassandra allowed both standard NoSQL operations and vector-based similarity searches in a single environment
• Scalability: Its distributed architecture ensured that even large volumes of content could be processed and accessed efficiently

This made it possible for the AI to generate inquiries that only retrieved the most contextually relevant information, guaranteeing that each output was based on correct and significant content.

“We chose Cassandra because it supports both vector and traditional operations, giving us the flexibility to build a powerful retrieval layer for question generation,” Nishant Agarwal noted.

3. OpenAI Models: Generating Exam-Ready Questions

At the core of most AI transformations is the use of OpenAI’s large language models. These models are fine-tuned and prompted in such a way that they could generate diverse, exam-quality questions with high consistency.

The OpenAI models allowed the system to:

• Produce multiple question formats (MCQs, fill-in-the-blank, true/false, etc.)
• Maintain a formal tone, consistent grammar, and appropriate difficulty level
• Customize outputs based on subject matter, question structure, and certification guidelines
• Incorporate SME feedback dynamically—adapting over time to better meet organizational standards

Perhaps most importantly, these models made it possible to generate first drafts automatically, allowing SMEs to act as reviewers rather than authors.

Using OpenAI models, we could align the tone, grammar, and structure of each question to meet the certification body’s standards, while also upgrading model behavior during operations,” said Nishant Agarwal. 

Key Benefits Observed

Once implemented, the system delivered measurable outcomes:

• Reduced manual intervention in content processing and question drafting
• Faster turnaround for new content integration into exams
• Greater standardization in tone, grammar, and difficulty
• Lower operational cost compared to manual workflows
• Improved use of SME time—now focused on refining exam flow and quality

Most notably, the subject matter experts themselves benefited:

The biggest win was that SMEs could now focus more on exam design and quality, rather than typing and data entry,” added Nishant Agarwal in that webinar. 

Conclusion: Let Experts Focus on Expertise

Generative AI is not here to replace human experts rather, they can help in removing inefficiencies, standardize routine processes, and allow experts to focus on strategic, creative, and high-impact tasks.

By automating repetitive elements of the exam creation workflow, organizations not only save time and cost but also significantly enhance the quality and scalability of their assessments.

As shown in Ksolves’ implementation, AI, when paired with human oversight, becomes a force multiplier—empowering subject matter experts to deliver better outcomes, faster.

Watch the full webinar from Ksolves to learn how real-world AI systems are being used to streamline assessment creation in the education and certification industries.

Ready to enhance your content workflows with AI? Contact Ksolves to explore a tailored solution for your organization.