What's included

• Data Preparation and Preprocessing

  - Extraction of data from client-provided or public sources. - Data cleaning and normalization to ensure consistency. - Enrichment and metadata aggregation for contextual relevance.

• Vectorization and Storage

  - Implementation of embedding models for data vectorization. - Setup of a vector database for efficient retrieval and indexing.

• Chain Development

  - Design and development of the retrieval-augmented generation (RAG) chain. - Integration with the chosen LLM to handle queries and generate responses.

• Prompt Engineering

  - Crafting and testing of prompts to optimize model responses. - Iterative refinement for specific use cases, including edge scenarios.

• Model Deployment

  - Deployment of the RAG solution as a scalable and secure API endpoint. - Integration with the client’s existing infrastructure or tools.

• Custom Application Integration (Optional)

  - Building a custom chatbot or interface for specific platforms, such as Microsoft Teams, Slack, or a web application.

• Testing and Validation

  - Functional and performance testing of the RAG solution. - Validation of the response quality against predefined benchmarks.

• Documentation and Support

  - Technical documentation for the chain, APIs, and overall architecture. - User guide for interacting with the solution. - Initial support and handover training for the client’s team.