Advanced Retrieval-Augmented Generation (RAG) chatbot developed for Shiftpartner, a UK-based workforce management platform, providing intelligent conversational assistance to users.
Initially built on OpenAI GPT-3.5 with sophisticated function calling capabilities to augment prompts with relevant contextual information based on user queries, currently in beta deployment within the application.
Implementing migration to AWS infrastructure using Claude v2 with serverless Lambda functions, featuring 22+ implemented functions and advanced context retention for truly contextual multi-turn conversations.
Shiftpartner users needed quick access to platform information, shift details, and operational guidance but faced challenges navigating complex interfaces and documentation to find relevant answers.
Traditional static FAQs and knowledge bases couldn't provide personalized, context-aware responses or understand natural language queries with varying levels of specificity and user intent.
The platform required an intelligent assistant capable of retrieving precise information from multiple data sources while maintaining conversation context across multiple user interactions and sessions.
RAG Architecture - Retrieval-Augmented Generation combining large language models with dynamic information retrieval
OpenAI GPT-3.5 Integration - Initial implementation leveraging GPT-3.5 for natural language understanding and response generation
Function Calling Framework - 22+ specialized functions augmenting prompts with relevant contextual data based on user queries
Context Retention System - Advanced conversation memory maintaining contextual awareness across multi-turn dialogues
AWS Claude v2 Migration - Ongoing transition to AWS infrastructure using Anthropic's Claude v2 for enhanced capabilities
Serverless Lambda Architecture - Scalable, cost-effective deployment using AWS Lambda functions for chatbot logic
Dynamic Prompt Augmentation - Intelligent system selecting and injecting relevant information into prompts based on user intent
Multi-Source Data Integration - Unified access to shift schedules, user profiles, policies, and operational data
Beta Testing Framework - Controlled rollout within application for real-world validation and iterative improvements
Conversational Analytics - Monitoring and optimization based on user interactions and satisfaction metrics
Our development approach focused on creating an intelligent, context-aware conversational assistant that seamlessly retrieves and presents relevant information from Shiftpartner's diverse data sources.
We implemented a phased strategy starting with OpenAI GPT-3.5 for rapid deployment and beta testing, followed by AWS migration to Claude v2 for enhanced performance and cost optimization.
Designed comprehensive RAG architecture with 22+ specialized functions covering shift management, user queries, policy information, and operational guidance.
Implemented intelligent function selection and prompt augmentation logic ensuring relevant contextual information is retrieved based on user intent analysis.
Deployed initial OpenAI GPT-3.5-based chatbot in beta within Shiftpartner application for real-world testing and validation.
Developed advanced context retention system maintaining conversation history and user-specific state across multi-turn dialogues for natural interactions.
Architected AWS-based infrastructure using Lambda functions for serverless scalability and cost optimization.
Implementing migration to Claude v2 providing enhanced reasoning capabilities, longer context windows, and improved response quality for complex workforce management queries.
Successfully deployed RAG-based chatbot in beta within Shiftpartner application, providing users with intelligent, context-aware assistance for shift management and platform navigation.
The 22+ implemented functions enable comprehensive information retrieval covering diverse user needs, while context retention creates natural, human-like conversational experiences across multiple interactions.
Ongoing AWS migration to Claude v2 with Lambda architecture positions the solution for enhanced scalability, reduced operational costs, and improved response quality as usage expands beyond beta deployment.
