Client Success Story

Prompt Engineering and Optimization for a Custom-Trained GPT Model to Reduce Consumer Bounce Rate


A US-Based Organic Produce Provider

Our client is a leading provider of organic fruits and vegetables, committed to delivering fresh, high-quality produce to customers across the United States. They have large farms in California where customers can explore seasonal produce and learn about sustainable farming practices. They also have a website allowing distant customers to purchase and get a pan-USA delivery of organic products. To enhance the shopping experience, they have integrated a virtual assistant bot that helps visitors navigate the website.


Identifying Key Issues in User Experience

Aiming to scale their online business, our client intensified their advertising efforts, which led to a significant increase in website traffic. However, this surge in visits did not translate into higher sales. Instead, they experienced a troubling rise in their bounce rate.

We examined their website and figured out the root cause - the erratic performance of their virtual assistant bot, which was crucial for guiding customers through the purchasing process. This was because:

  • Despite automation, the response time for customer queries was high.
  • The bot often struggled with complex user queries and provided inaccurate information, causing confusion and frustration among visitors.
  • It failed to offer personalized recommendations for signed-in customers.

Prompt Engineering for a Custom-Trained Conversational AI Chatbot for Specific Chat Flows

Having understood their business and specific challenges concerning the existing virtual assistant bot, we assembled a dedicated team of ChatGPT programmers, working around the clock to ensure timely delivery. Here is what they did:


Set up Algorithms and Installed Necessary Libraries

We selected and set up the appropriate machine learning and natural language processing (NLP) frameworks and libraries, including:

  • Transformers (Hugging Face): For handling GPT-4 and other pre-trained transformer models with text, video, and audio modalities
  • Tokenizers (Hugging Face): To tokenize input text into data that can be processed (by the model) for training and inference
  • SpaCy: For entity recognition and dependency parsing
  • NLTK Toolkit: For text processing
  • FastText: For word embeddings
  • Gensim: For topic modeling and word embedding tasks

Setting up these libraries ensured that the environment was prepared for sophisticated chatbot solution development and integration tasks.


Engineered Prompts for Specific Chat Flow

Next, our ChatGPT developers went through the client’s proprietary data and user queries to understand where the bot lacked. After analyzing, they created a series of tailored prompts and their responses to compile a comprehensive training dataset, ensuring the chatbot could handle a wide range of consumer queries. This was done in the following steps:

  • Defining conversation intents: Identifying the key types of customer interactions (e.g., product inquiries, order tracking, general FAQs)
  • Creating training data: Compiling the dataset of sample dialogues relevant to the virtual assistant's functionality
  • Fine-tuning the model: Training the pre-trained GPT-4 model using the training data to improve its performance on specific chat flows
  • Prompt Optimization: Adjusting the wording, length, and structure of the prompts to maximize the model's performance

Custom-Trained a GPT-4 Model Using the Engineered Prompts

We ensured that the GPT-4 model could handle the client's specific use cases and provide accurate, contextually relevant responses. This training process involved:

  • Model Training: Using the prepared dataset to fine-tune the GPT-4 model, enhancing its ability to understand and respond to a variety of customer queries.
  • Validation and Testing: Continuously testing the model's performance with real-world scenarios to ensure accuracy and reliability.
  • Iterative Improvement: Refining the model through iterative cycles of training and feedback, optimizing it for better user interaction.

Implemented a Document Search System

A document search system was implemented to enhance the chatbot's ability to retrieve and provide information from the client's document repository. This involved:

  • Recognizing entities (e.g., product names, categories, dates, availability stats) from documents
  • Converting documents into numerical vectors using methods such as TF-IDF (Term Frequency-Inverse Document Frequency), Word2Vec, Bag of Words, or embeddings
  • Used cosine similarity and cosine distance to measure the distance and angle between the embedded vectors
  • Setting up a search algorithm to efficiently retrieve relevant documents based on user queries

API Integrations

To provide a seamless user experience, we integrated the chatbot with external services such as email sending, the CRM system, and a recommendation engine. Our ChatGPT integration services provided the following support:

  • Email: We integrated APIs such as SendGrid and AWS SES to enable the chatbot to send confirmation emails, follow-ups, and newsletters directly to users.
  • CRM Integration: Using the CRM’s API, we logged customer interactions, tracked leads, and updated customer information in real-time. This integration facilitated efficient customer relationship management and improved data accuracy.
  • Recommendation Engine: We integrated a recommendation engine to provide personalized product recommendations based on user behavior, preferences, and past purchases. This helped in boosting sales through tailored suggestions.

This ensured that all customer data was synchronized between the chatbot and the CRM system.


Deployed the Fine-Tuned Chatbot

As an AWS partner company, we utilized AWS SAM (Serverless Application Model) to ensure continuous integration and deployment (CI/CD) of the chatbot. This involved:

  • CI/CD Pipeline Setup: To automate the entire chatbot development process, we configured a CI/CD pipeline.
  • Serverless Deployment: Deploying the chatbot as a serverless application using AWS Lambda, API Gateway, and DynamoDB for scalable and cost-effective operations.

Technology Stack

ML Frameworks and Libraries

  • transformer
  • logo-spacy
  • nltk
  • fast
  • Gensim

Model Training and Fine-tuning

  • pytorch
  • tensorflow

Data Storage

  • amazon dynamodb vector logo

CI/CD and Serverless Deployment

  • aws pipeline
    AWS CodePipeline
  • codebuild
  • codedeploy
  • aws lambda logo
    AWS Lambda

Project Outcomes

80% improvement in response accuracy

30% higher conversions from intelligent cross-sell and upsell recommendations

45% reduced customer bounce rate

Overwhelming positive user feedback on shopping experiences on the website

40% reduction in operational costs


Boost Sales with Better, AI-Powered Customer Support

Our custom chatbot development services and prompt engineering expertise enabled the client to reduce customer bounce rate by over 45%. Learn how you can also boost conversions with our ChatGPT integration services.