LLM Fine-tuning for Content Management and Creation in EdTech

improvement in prompt accuracy
40-50%
faster time to content publication
LLM Fine-tuning for Content Management and Creation in EdTech | Crunch-IS Case Study
Discover how LLMs revolutionized content creation and management in EdTech, delivering streamlined and automated workflows for better outcomes.
Industry:

Technology, Information and Internet

Location:

USA

Team Size:

ML Engineer, Project manager

Duration:

6 months

Technologies
LLM
LangСhain
LangSmith
Python
Argilla
01

About the Client

We are collaborating with an education-centric company that has developed an AI-driven application to streamline the publishing process. Powered by AI, this company promotes self-publishing and is featured on the Inc. 5000 list.

LLM Fine-tuning for Content Management and Creation in EdTech | Crunch-IS Case Study
02

Challenge

The company faced limitation of their low-code platform (AirOps) that was used for building an AI flow within the app. They required an in-house expertise to unlock scalability and greater flexibility.

03

Solution we Delivered

To address the limitations of the existing system and meet the company’s goals for scalability and customization, we reimagined the general AI workflow and architecture. Our efforts were focused on three key areas:

Analysis and Audit of the Existing System

We assessed the existing system on AirOps, identifying inefficiencies in prompt workflows followed by customer experience bottlenecks such as limitations in scalability, customization, and data integration.

Prompt Engineering

Our next step was to redefine the prompt engineering process that lies in the core of the system. There was a multi-step prompts creation process for self-publishing that we improved with AI model optimization and prompts refining for accurate data extraction and generation.

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AI-Builder Architecture

Ensuring the stable work of the system, we designed a scalable solution using LangСhain. The new solution now includes modules for topic research, customer insights, book outlining that boost end-users experience for publishing. This new architecture ensures better customization, usability, and performance.

04

Client’s Results

The transition to custom AI solution brought significant improvements in several key areas, addressing the limitations of the previous platform and delivering tangible benefits to the company. These included enhanced cost efficiency, scalability, and usability.

60% increase in usability

Cost Efficiency

Transitioning to LangChain-based solutions reduced operational costs.

Scalability

The previous platform lacked scalability and customization, limiting project growth. By moving to LangChain, the company can now scale their application effectively.

Enhanced Usability

The prior low-code platform was unsuitable for users unfamiliar with LLM solutions. The new system offers better flexibility and usability for both technical and non-technical users.

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