What's included

• Effective custom-usecase LLM/Chatbot

  Objective: Develop a tailored language model or chatbot that meets the specific needs of the client. Activities: - Analyze client requirements and intended use cases. - Select and fine-tune the appropriate LLM (e.g., GPT-4, Gemini) or develop a custom chatbot. - Employ prompt engineering to achieve optimal results and ensure the model meets performance criteria. - Test the model to ensure it meets performance criteria. Deliverables: - Custom-trained LLM or chatbot tailored to the client's use case. - Performance reports and testing results.

• Deployable API Endpoint

  Objective: Provide a scalable and secure API endpoint for the client to interact with the LLM or chatbot. Activities: - Develop an API layer to facilitate communication with the LLM or chatbot. - Implement security measures to protect data and ensure authorized access. - Deploy the API endpoint on a cloud platform (serverless) or on-premises, using stack including but not limited to: Python, FastAPI, Flask, Go, Docker, Kubernetes, etc. - Conduct performance and load testing to ensure reliability. Deliverables: - Fully functional API endpoint. - API documentation and usage guidelines.

• Code handover and Technical Documentation

  Objective: Ensure the client has all necessary code and documentation to maintain and extend the solution. Activities: - Package all code and scripts used in the development process. - Develop comprehensive technical documentation, including setup instructions, code explanations, and maintenance guidelines. - Conduct a handover session to explain the codebase and answer any questions. Deliverables: - Complete codebase in a version-controlled repository. - Detailed technical documentation. - Handover session and recorded materials.

• Optional: Vector Database for RAG

  Objective: Enhance the LLM's capabilities with Retrieval-Augmented Generation (RAG) by integrating a vector database to increase subject expertise. Activities: - Set up and configure a vector database (e.g., AlloyDB for Postgres) to store and manage embeddings. - Integrate the vector database with the LLM to enable RAG functionalities. - Test the RAG setup to ensure accurate and efficient retrieval of relevant information. Deliverables: - Configured vector database with client-specific data. - Integration with the LLM for RAG capabilities. - Performance and accuracy reports.

• Optional: Terraform and Cloud environment handover

  Objective: Provide infrastructure as code to automate the deployment of the AI solution in a cloud environment. Activities: - Develop Terraform scripts to provision and configure necessary cloud resources. - Deploy the infrastructure using Terraform and validate the setup. - Document the Terraform code and provide a handover session to ensure the client can manage the infrastructure. Deliverables: - Terraform scripts and modules. - Fully deployed cloud environment. - Documentation and handover session for managing the infrastructure.